The Experts below are selected from a list of 1353 Experts worldwide ranked by ideXlab platform
Michel C Desmarais - One of the best experts on this subject based on the ideXlab platform.
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feature selection by efficient learning of Markov Blanket
2010Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract: Abstract—Markov Blanket was proved as the theoretically optimal feature subset to predict the target. IPC-MB was firstly proposed in 2008 to induce the Markov Blanket via local search, and it is believed important progress as compared with previously published work, like IAMB, PCMB and PC. However, the proof appearing in its first publication is not complete and sound enough. In this paper, we revisit IPC-MB with discussion as not found in the original paper, especially on the proof of its theoretical correctness. Besides, experimental studies with small to large scale of problems (Bayesian networks) are conducted and the results demonstrate that IPC- MB achieves much higher accuracy than IAMB, and much better time efficiency than PCMB and PC.
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fast Markov Blanket discovery algorithm via local learning within single pass
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence 21st conference on Advances in artificial intelligence, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Learning of Markov Blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.
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Canadian Conference on AI - Fast Markov Blanket discovery algorithm via local learning within single pass
Advances in Artificial Intelligence, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Learning of Markov Blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.
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tradeoff analysis of different Markov Blanket local learning approaches
Knowledge Discovery and Data Mining, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Discovering the Markov Blanket of a given variable can be viewed as a solution for optimal feature subset selection. Since 1996, several algorithms have been proposed to do local search of the Markov Blanket, and they are proved to be much more efficient than the traditional approach where the whole Bayesian Network has to be learned first. In this paper, we compare those known published algorithms, including KS, GS, IAMB and its variants, PCMB, and one newly proposed called BFMB. We analyze the theoretical basis and practical values of each algorithm with the aim that it will help applicants to determine which ones to take in their specific scenarios.
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PAKDD - Tradeoff analysis of different Markov Blanket local learning approaches
Advances in Knowledge Discovery and Data Mining, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Discovering the Markov Blanket of a given variable can be viewed as a solution for optimal feature subset selection. Since 1996, several algorithms have been proposed to do local search of the Markov Blanket, and they are proved to be much more efficient than the traditional approach where the whole Bayesian Network has to be learned first. In this paper, we compare those known published algorithms, including KS, GS, IAMB and its variants, PCMB, and one newly proposed called BFMB. We analyze the theoretical basis and practical values of each algorithm with the aim that it will help applicants to determine which ones to take in their specific scenarios.
Qiang Ji - One of the best experts on this subject based on the ideXlab platform.
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Efficient Markov Blanket Discovery and Its Application
IEEE Transactions on Cybernetics, 2017Co-Authors: Qiang JiAbstract:In a Bayesian network (BN), a target node is independent of all other nodes given its Markov Blanket (MB), and finding the MB has many applications, including feature selection and BN structure learning. We propose a new MB discovery algorithm, simultaneous MB (STMB), to improve the efficiency of the existing topology-based MB discovery algorithms. The proposed method removes the necessity of enforcing the symmetry constraint that is prevalent in existing algorithms, by exploiting the coexisting property between spouses and descendants of the target node. Since STMB mainly reduces the number of independence tests needed to complete the MB set after finding the parents-and-children set, it is applicable to all previous topology-based methods. STMB is both sound and complete. Experiments show that STMB has a comparable accuracy but much better efficiency than state-of-the-art methods. An application on benchmark feature selection datasets further demonstrates the excellent performance of STMB.
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efficient score based Markov Blanket discovery
International Journal of Approximate Reasoning, 2017Co-Authors: Qiang JiAbstract:In a Bayesian Network (BN), the Markov Blanket (MB) of a target node consists of its parents, children, and spouses, and the target node is independent of all other nodes given its MB. Finding the MB has many applications, including feature selection and BN structure learning. We propose two new Markov Blanket discovery algorithms, score-based Simultaneous Markov Blanket discovery ( S 2 TMB) and its more efficient variant, S 2 TMB+, to improve the efficiency of existing score-based MB learning algorithms. The proposed methods remove the necessity of enforcing the commonly used symmetry constraint by exploiting the coexistence property between spouses and descendants of the target node. S 2 TMB and S 2 TMB+ achieve comparable accuracy and better efficiency than state-of-the-art score-based methods. S 2 TMB and S 2 TMB+ are proven sound and complete under one conjecture. Empirical results on standard MB discovery datasets demonstrate the superior performances of S 2 TMB and S 2 TMB+. We introduce a new score-based Markov Blanket Discovery algorithm and its variants.We show a coexist property between false positive PC nodes and spouses in the score-based MB discovery approach.The new algorithms improve the efficiency over the state-of-the-art score-based algorithm.The new algorithms are proven sound and complete under one conjecture.
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Hybrid Markov Blanket discovery
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Co-Authors: Qiang JiAbstract:In a Bayesian Network (BN), a target node is independent of all other nodes given its Markov Blanket (MB). By finding the MB, many problem can be solved directly or indirectly. There exist predominately two different approaches to finding the MB: the score-based and the constraint-based algorithms. We introduce a new Markov Blanket learning algorithm, Hybrid Markov Blanket (HMB) discovery, by combining these two different approaches. Specifically, HMB first employs a score-based method for finding the parents and children (PC) of the target node. HMB then introduces an efficient constraint-based approach to finding target node's spouses without enforcing the symmetry constraint that is required by existing constraint-based methods. In comparison, HMB achieves a better accuracy than the traditional constraint-based approaches and a better efficiency than the existing score-based approaches. In addition, HMB is theoretically proven sound and complete. Empirical results on synthetic and standard MB discovery datasets demonstrate the superior performance of HMB.
Shunkai Fu - One of the best experts on this subject based on the ideXlab platform.
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efficient learning of Markov Blanket and Markov Blanket classifier
2010Co-Authors: Shunkai FuAbstract:RESUME La selection de variables est un probleme de premiere importance dans le domaine de l'apprentissage machine et le forage de donnees. Pour une tâche de classification, un jalon important du developpement de strategies selection de variables a ete atteint par Koller et Shamai [1]. Sur la base des travaux de Pearl dans le domaine des reseaux bayesiens (RB) [2], ils ont demontre que la couverture de Markov (CM) d'une variable nominale represente le sous-ensemble optimal pour predire sa valeur (classe). Differents algorithmes ont ete developpes pour d'induire la CM d'une variable cible a partir de donnees, sans pour autant necessiter l'induction du RB qui inclue toutes les variables potentielles depuis 1996, mais ils affichent tous des problemes de performance, soit au plan de la complexite calculatoire, soit au plan de la reconnaissance. La premiere contribution de cette these est le developpement d'un nouvel algorithme pour cette tâche. L'algorithme IPC-MB [9-11] permet d'induire la CM d'une variable avec une performance qui combine les meilleures performances en terme de complexite calculatoire et de reconnaissance. IPC-MB effectue une recherche iterative des parents et enfants du noeud cible en minimisant le nombre de variables conditionnnelles des tests d'independance. Nous prouvons que l'algorithme est theoriquement correct et comparons sa performance avec les algorithmes les mieux connus, IAMB [12], PCMB [13] et PC [14]. Des experiences de simulations en utilisant des donnees generees de reseaux bayesiens connus, a savoir un reseau de petite envergure, Asia, contenant huit noeuds; deux reseaus de moyenne envergure, Alarm et PolyAlarm de 37 noeuds, et deux reseaux de plus grande envergure, Hailfinder contenant 56 noeuds et Test152 contenant 152 noeuds. Les resultats demontrent qu'avec un nombre comparable d'observations, (1) IPC-MB obtient une reconnaissance nettement plus elevee que IAMB, jusqu'a 80% de reduction de distance (par rapport a un resultat parfait), (2) IPC-MB a une reconnaissance legerement superieure que PCMB et PC, et (3) IPC-MB necessite jusqu'a 98% moins de tests conditionnels que PC et 95% de moins que PCMB (le nombre de tests conditionnels represente la mesure de complexite calculatoire ici). La seconde contribution de la these est un algorithme pour induire la topologie du RB constitue des variables de la CM. Lorsqu'une CM d'une variable cible forme un RB, ce reseau est alors considere comme un classificateur, nomme une Couverture de Markov de Classification (MBC). L'algorithme a ete nomme IPC-MBC sur la base du premier algorithme, IPC-MB. A l'instar de IPC-MB, l'algorithme IPC-MBC effectue une serie de recherches locales pour eliminer les faux-negatifs, incluant les noeuds et les arcs. Cependant, sa complexite est superieure et requiert des ressources calculatoires plus importantes que IPC-MB. Nous prouvons que IPC-MB est theoriquement et effectuons des etudes empiriques pour comparer sa performance calculatoire et de reconnaissance par rapport a PC seul et PC combine a IPC-MB (c.-a-d. l'induction de la structure du RB avec l'algorithme PC seul et avec PC applique sur le resultat de IPC-MB). Les memes donnees que pour les experiences de simulation de IPC-MB sont utilisees. Les resultats demontrent que IPC-MBC combine a IPC-MB et que PC combine a IPC-MB sont tous deux plus efficaces que PC seul en termes de temps de complexite calculatoires, fournissant jusqu'a 95% de reduction du nombre de tests conditionnels, sans pour autant avoir d'impact au plan du taux de reconnaissance.----------ABSTRACT Feature selection is a fundamental topic in data mining and machine learning. It addresses the issue of dimension reduction by removing non-relevant, or less relevant attributes in model building. For the task of classification, a major milestone for feature selection was achieved by Koller and Sahami [1]. Building upon the work of Pearl on Bayesian Networks (BN) [2], they proved that a Markov Blanket (MB) of a variable is the optimal feature subset for class prediction. Deriving the MB of a class variable given a BN is a trivial problem. However, learning the structure of a BN from data is known to be NP hard. For large number of variables, learning the BN is impractical, not only because of the computational complexity, but also because of the data size requirement that is one of the curses of high dimensionality feature spaces. Hence, simpler topologies are often assumed, such as the Naive Bayes approach (NB) [5, 6], which is probably the best known one due its computational simplicity, requiring no structure learning, and also its surprising effectiveness in many applications despite its unrealistic assumptions. One of its extension, Tree-Augmented Naive Bayes (TAN) [7] is shown to have a better performance than NB, by allowing limited additional dependencies among the features. However, because they make strong assumptions, these approaches may be flawed in general. By further relaxing the restriction on the dependencies, a BN is expected to show better performance in term of classification accuracy than NB and TAN [8]. The question is whether we can derive a MB without learning the full BN topology for the classification task. Let us refer to a MB for classification as a Markov Blanket Classifier, MBC. The MBC is expected to perform as well as the whole Bayesian network as a classifier, though it is generally much smaller in size than the whole network. This thesis addresses the problem of deriving the MBC effectively and efficiently from limited data. The goal is to outperform the simpler NB and TAN approaches that rely on potentially invalid assumptions, yet to allow MBC learning with limited data and low computational complexity. Our first contribution is to propose one novel algorithm to filter out non-relevant attributes of a MBC. From our review, it is known that there are at least nine existing published works on the learning of Markov Blanket since 1996. However, there is no satisfactory tradeoff between correctness, data requirement and time efficiency. To address this tradeoff, we propose the IPC-MB algorithm [9-11]. IPC-MB performs an iterative search of the parents and children given a node of interest. We prove that the algorithm is sound in theory, and we compare it with the state of the art in MB learning, IAMB [12], PCMB [13] and PC [14]. Experiments are conducted using samples generated from known Bayesian networks, including small one like Asia with eight nodes, medium ones like Alarm and PolyAlarm (one polytree version of Alarm) with 37 nodes, and large ones like Hailfinder (56 nodes) and Test152 (152 nodes). The results demonstrate that, given the same amount of observations, (1) IPC-MB achieves much higher accuracy than IAMB, up to 80% reduction in distance (from the perfect result), (2) IPC-MB has slightly higher accuracy than PCMB and PC, (3) IPC-MB may require up to 98% fewer conditional independence (CI) tests than PC, and 95% fewer than PCMB. Given the output of IPC-MB, conventional structure learning algorithms can be applied to recover MBC without any modification since the feature selection procedure is transparent to them. In fact, the output of IPC-MB can be viewed as the output of general feature selection, and be employed further by all kinds of classifier. This algorithm was implemented by the author while working at SPSS and shipped with the software Clementine 12 in 2007. The second contribution is to extend IPC-MB to induce the MBC directly without having to depend on external structure learning algorithm, and the proposed algorithm is named IPC-MBC (or IPC-BNC in one of our early publication) [15]. Similar to IPC-MB, IPC-MBC conducts a series of local searches to filter out false negatives, including nodes and arcs. However, it is more complex and requires greater computing resource than IPC-MB. IPC-MBC is also proved sound in theory. In our empirical studies, we compare the accuracy and time cost between IPC-MBC, PC and IPC-MB plus PC (i.e. structure learning by PC on the features output by IPC-MB), with the same data as used in the study of IPC-MB. It is observed that both IPC-MBC and IPC-MB plus PC are much more time efficient than PC, with up to 95% saving of CI tests, but with no loss of accuracy. This reflects the advantage of local search and feature selection respectively.
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feature selection by efficient learning of Markov Blanket
2010Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract: Abstract—Markov Blanket was proved as the theoretically optimal feature subset to predict the target. IPC-MB was firstly proposed in 2008 to induce the Markov Blanket via local search, and it is believed important progress as compared with previously published work, like IAMB, PCMB and PC. However, the proof appearing in its first publication is not complete and sound enough. In this paper, we revisit IPC-MB with discussion as not found in the original paper, especially on the proof of its theoretical correctness. Besides, experimental studies with small to large scale of problems (Bayesian networks) are conducted and the results demonstrate that IPC- MB achieves much higher accuracy than IAMB, and much better time efficiency than PCMB and PC.
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fast Markov Blanket discovery algorithm via local learning within single pass
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence 21st conference on Advances in artificial intelligence, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Learning of Markov Blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.
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Canadian Conference on AI - Fast Markov Blanket discovery algorithm via local learning within single pass
Advances in Artificial Intelligence, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Learning of Markov Blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.
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tradeoff analysis of different Markov Blanket local learning approaches
Knowledge Discovery and Data Mining, 2008Co-Authors: Shunkai Fu, Michel C DesmaraisAbstract:Discovering the Markov Blanket of a given variable can be viewed as a solution for optimal feature subset selection. Since 1996, several algorithms have been proposed to do local search of the Markov Blanket, and they are proved to be much more efficient than the traditional approach where the whole Bayesian Network has to be learned first. In this paper, we compare those known published algorithms, including KS, GS, IAMB and its variants, PCMB, and one newly proposed called BFMB. We analyze the theoretical basis and practical values of each algorithm with the aim that it will help applicants to determine which ones to take in their specific scenarios.
Kui Yu - One of the best experts on this subject based on the ideXlab platform.
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discovering Markov Blanket from multiple interventional datasets
arXiv: Artificial Intelligence, 2018Co-Authors: Kui Yu, Jiuyong LiAbstract:In this paper, we study the problem of discovering the Markov Blanket (MB) of a target variable from multiple interventional datasets. Datasets attained from interventional experiments contain richer causal information than passively observed data (observational data) for MB discovery. However, almost all existing MB discovery methods are designed for finding MBs from a single observational dataset. To identify MBs from multiple interventional datasets, we face two challenges: (1) unknown intervention variables; (2) nonidentical data distributions. To tackle the challenges, we theoretically analyze (a) under what conditions we can find the correct MB of a target variable, and (b) under what conditions we can identify the causes of the target variable via discovering its MB. Based on the theoretical analysis, we propose a new algorithm for discovering MBs from multiple interventional datasets, and present the conditions/assumptions which assure the correctness of the algorithm. To our knowledge, this work is the first to present the theoretical analyses about the conditions for MB discovery in multiple interventional datasets and the algorithm to find the MBs in relation to the conditions. Using benchmark Bayesian networks and real-world datasets, the experiments have validated the effectiveness and efficiency of the proposed algorithm in the paper.
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A novel android malware detection method based on Markov Blanket
Proceedings - 2016 IEEE 1st International Conference on Data Science in Cyberspace DSC 2016, 2017Co-Authors: Xiaotian Zhang, Yuqi Fan, Donghui Hu, Kui YuAbstract:© 2016 IEEE. Android platform is the most popular mobile operating system. With the increasing market share of Android smartphone, Android malware appears in many Android markets, and it has imposed a high risk on users. There have been some approaches focusing on permissions and API calls with machine-learning methods. However, most of the approaches do not consider the feature selection problem. In this paper, we propose a new method based on Markov Blanket for Android malicious application detection. It have proven that in a faithful Bayesian network, the Markov Blanket of a class attribute is a set of optimal features for feature selection for classification. We collect 5,553 malware and 3,449 benign applications to learn Markov Blanket as our feature set, and use SVM to test our method. The experiment results show that our method can reduce the dimensions of feature set and keep a high accuracy with approximately 96% in Android malware detection.
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Markov Blanket Feature Selection Using Representative Sets
IEEE Transactions on Neural Networks and Learning Systems, 2017Co-Authors: Kui Yu, Xindong Wu, Yang Mu, Wei Ding, Hao WangAbstract:It has received much attention in recent years to use Markov Blankets in a Bayesian network for feature selection. The Markov Blanket of a class attribute in a Bayesian network is a unique yet minimal feature subset for optimal feature selection if the probability distribution of a data set can be faithfully represented by this Bayesian network. However, if a data set violates the faithful condition, Markov Blankets of a class attribute may not be unique. To tackle this issue, in this paper, we propose a new concept of representative sets and then design the selection via group alpha-investing (SGAI) algorithm to perform Markov Blanket feature selection with representative sets for classification. Using a comprehensive set of real data, our empirical studies have demonstrated that SGAI outperforms the state-of-the-art Markov Blanket feature selectors and other well-established feature selection methods.
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DSC - A Novel Android Malware Detection Method Based on Markov Blanket
2016 IEEE First International Conference on Data Science in Cyberspace (DSC), 2016Co-Authors: Xiaotian Zhang, Donghui Hu, Kui YuAbstract:Android platform is the most popular mobile operating system. With the increasing market share of Android smartphone, Android malware appears in many Android markets, and it has imposed a high risk on users. There have been some approaches focusing on permissions and API calls with machine-learning methods. However, most of the approaches do not consider the feature selection problem. In this paper, we propose a new method based on Markov Blanket for Android malicious application detection. It have proven that in a faithful Bayesian network, the Markov Blanket of a class attribute is a set of optimal features for feature selection for classification. We collect 5,553 malware and 3,449 benign applications to learn Markov Blanket as our feature set, and use SVM to test our method. The experiment results show that our method can reduce the dimensions of feature set and keep a high accuracy with approximately 96% in Android malware detection.
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ICDM - Markov Blanket Feature Selection with Non-faithful Data Distributions
2013 IEEE 13th International Conference on Data Mining, 2013Co-Authors: Kui Yu, Xindong Wu, Zan Zhang, Yang Mu, Hao Wang, Wei DingAbstract:In faithful Bayesian networks, the Markov Blanket of the class attribute is a unique and minimal feature subset for optimal feature selection. However, little attention has been paid to Markov Blanket feature selection in a non-faithful environment which widely exists in the real world. To tackle this issue, in this paper, we deal with non-faithful data distributions and propose the concept of representative sets instead of Markov Blankets. With a standard sparse group lasso for selection of features from the representative sets, we design an effective algorithm, SRS, for Markov Blanket feature Selection via Representative Sets with non-faithful data distributions. Empirical studies demonstrate that SRS outperforms the state-of-the-art Markov Blanket feature selectors and other well-established feature selection methods.
K.p. Rosenblatt - One of the best experts on this subject based on the ideXlab platform.
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an extended Markov Blanket approach to proteomic biomarker detection from high resolution mass
2009Co-Authors: P. Gurnani, J. Schorge, K.p. RosenblattAbstract:High-resolution matrix-assisted laser desorp- tion/ionization time-of-flight mass spectrometry has recently shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in finding such patterns is the large number of mass/charge peaks (features, biomarkers, data points) in a spectrum. To tackle this problem, we have developed methods for data preprocessing and biomarker selection. The preprocessing consists of binning, baseline correction, and normalization. An algorithm, extended Markov Blanket, is developed for biomarker detection, which combines redundant feature removal and discriminant feature selection. The biomarker selection couples with support vector ma- chine to achieve sample prediction from high-resolution proteomic profiles. Our algorithm is applied to recurrent ovarian cancer study that contains platinum-sensitive and platinum-resistant samples after treatment. Experiments show that the proposed method performs better than other feature selection algorithms. In particular, our algorithm yields good performance in terms of both sensitivity and specificity as compared to other methods.
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An Extended Markov Blanket Approach to Proteomic Biomarker Detection From High-Resolution Mass Spectrometry Data
IEEE Transactions on Information Technology in Biomedicine, 2009Co-Authors: Jung Hun Oh, P. Gurnani, J. Schorge, K.p. Rosenblatt, Jean X. GaoAbstract:High-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has recently shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in finding such patterns is the large number of mass/charge peaks (features, biomarkers, data points) in a spectrum. To tackle this problem, we have developed methods for data preprocessing and biomarker selection. The preprocessing consists of binning, baseline correction, and normalization. An algorithm, extended Markov Blanket, is developed for biomarker detection, which combines redundant feature removal and discriminant feature selection. The biomarker selection couples with support vector machine to achieve sample prediction from high-resolution proteomic profiles. Our algorithm is applied to recurrent ovarian cancer study that contains platinum-sensitive and platinum-resistant samples after treatment. Experiments show that the proposed method performs better than other feature selection algorithms. In particular, our algorithm yields good performance in terms of both sensitivity and specificity as compared to other methods.
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CIBCB - Multicategory Classification using Extended SVM-RFE and Markov Blanket on SELDI-TOF Mass Spectrometry Data
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005Co-Authors: Jung Hun Oh, A. Nandi, P. Gurnani, J. Schorge, Lynne M. Knowles, K.p. RosenblattAbstract:Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers for disease to help early detection of the disease. Recently, support vector machine (SVM) algorithm based on recursive feature elimination (RFE) was proposed to find a set of genes for cancer classification. In our study, we extend the SVM-RFE such that it can be used in the multicategory classification work using SELDI-TOF mass spectrometry data and propose a new feature selection algorithm (SVM-MB/RFE : SVM-Markov Blanket/Recursive Feature Elimination). In the preprocessing task of SVM-MB/RFE, ANOVA (Analysis of Variance) and binning methods are used for feature filtering. We demonstrate that the performance is improved through the preprocessing work. Compared with other methods such as not only SVM-RFE and Markov Blanket but also PCA (Principle Components Analysis)+LDA (Linear Discriminant Analysis) and other feature selection algorithms, SVM-MB/RFE performs better than them.
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Multicategory Classification using Extended SVM-RFE and Markov Blanket on SELDI-TOF Mass Spectrometry Data
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005Co-Authors: Jung Hun Oh, A. Nandi, P. Gurnani, L. Knowles, J. Schorge, K.p. RosenblattAbstract:Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers for disease to help early detection of the disease. Recently, support vector machine (SVM) algorithm based on recursive feature elimination (RFE) was proposed to find a set of genes for cancer classification. In our study, we extend the SVM-RFE such that it can be used in the multicategory classification work using SELDI-TOF mass spectrometry data and propose a new feature selection algorithm (SVM-MB/RFE : SVM-Markov Blanket/Recursive Feature Elimination). In the preprocessing task of SVM-MB/RFE, ANOVA (Analysis of Variance) and binning methods are used for feature filtering. We demonstrate that the performance is improved through the preprocessing work. Compared with other methods such as not only SVM-RFE and Markov Blanket but also PCA (Principle Components Analysis)+LDA (Linear Discriminant Analysis) and other feature selection algorithms, SVM-MB/RFE performs better than them.