The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform

Fan Yang - One of the best experts on this subject based on the ideXlab platform.

  • Distance Metric Learning-Based Conformal Predictor
    2012
    Co-Authors: Fan Yang, Zhigang Chen, Guifang Shao, Huazhen Wang
    Abstract:

    In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a Nonconformity Measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm.

  • AIAI (2) - Distance metric learning-based conformal predictor
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Fan Yang, Zhigang Chen, Guifang Shao, Huazhen Wang
    Abstract:

    In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a Nonconformity Measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Abstract Background Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

  • Hedged Predictions for Traditional Chinese Chronic Gastritis Diagnosis with Confidence Machine
    2008 International Conference on Computer Science and Information Technology, 2008
    Co-Authors: Huazhen Wang, Chengde Lin, Fan Yang
    Abstract:

    Traditional Chinese chronic gastritis diagnosis focuses on producing an accurate classifier and uncovering the predictive confidence for individual instance. Transductive confidence machine (TCM), which is a novel framework that provides hedged prediction coupled with valid confidence. In the framework of TCM, the efficiency of prediction depends on the Nonconformity Measure of samples. This paper incorporates random forests (RF) to propose a new TCM algorithm named TCM-RF. Our method benefits from the more precise and robust Nonconformity Measure. A case study of traditional Chinese chronic gastritis demonstrates that TCM-RF is feasible and effective.

Huazhen Wang - One of the best experts on this subject based on the ideXlab platform.

  • Distance Metric Learning-Based Conformal Predictor
    2012
    Co-Authors: Fan Yang, Zhigang Chen, Guifang Shao, Huazhen Wang
    Abstract:

    In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a Nonconformity Measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm.

  • AIAI (2) - Distance metric learning-based conformal predictor
    IFIP Advances in Information and Communication Technology, 2012
    Co-Authors: Fan Yang, Zhigang Chen, Guifang Shao, Huazhen Wang
    Abstract:

    In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a Nonconformity Measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Abstract Background Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

  • Hedged Predictions for Traditional Chinese Chronic Gastritis Diagnosis with Confidence Machine
    2008 International Conference on Computer Science and Information Technology, 2008
    Co-Authors: Huazhen Wang, Chengde Lin, Fan Yang
    Abstract:

    Traditional Chinese chronic gastritis diagnosis focuses on producing an accurate classifier and uncovering the predictive confidence for individual instance. Transductive confidence machine (TCM), which is a novel framework that provides hedged prediction coupled with valid confidence. In the framework of TCM, the efficiency of prediction depends on the Nonconformity Measure of samples. This paper incorporates random forests (RF) to propose a new TCM algorithm named TCM-RF. Our method benefits from the more precise and robust Nonconformity Measure. A case study of traditional Chinese chronic gastritis demonstrates that TCM-RF is feasible and effective.

Wei-wen Cai - One of the best experts on this subject based on the ideXlab platform.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

  • Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
    BMC Bioinformatics, 2009
    Co-Authors: Fan Yang, Huazhen Wang, Chengde Lin, Wei-wen Cai
    Abstract:

    Abstract Background Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion The method of using RF outlier Measure to design a Nonconformity Measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

John Shawe-taylor - One of the best experts on this subject based on the ideXlab platform.

  • A Nonconformity Approach to Model Selection for SVMs
    arXiv: Machine Learning, 2009
    Co-Authors: David Roi Hardoon, Zakria Hussain, John Shawe-taylor
    Abstract:

    We investigate the issue of model selection and the use of the Nonconformity (strangeness) Measure in batch learning. Using the Nonconformity Measure we propose a new training algorithm that helps avoid the need for Cross-Validation or Leave-One-Out model selection strategies. We provide a new generalisation error bound using the notion of Nonconformity to upper bound the loss of each test example and show that our proposed approach is comparable to standard model selection methods, but with theoretical guarantees of success and faster convergence. We demonstrate our novel model selection technique using the Support Vector Machine.

  • Support Vector Machine Model Selection Using Strangeness
    2009
    Co-Authors: David Roi Hardoon, Zakria Hussain, John Shawe-taylor
    Abstract:

    Model Selection is the task of choosing the best model for a particular data analysis task. It generally makes a compromise between fit with the data and the complexity of the model. Currently the most popular techniques used by practitioners are Cross-Validation (CV) and Leave-One-Out (LOO). In this study we concentrated on the Support Vector Machine (SVM) (Boser et al., 1992) model. Recently, Ozogur-Akyuz et al. (In Press), following on work by Ozogur et al. (2008), show that selecting a model whose hyperplane achieves the maximum separation from a test point obtains comparable error rates to those found by selecting the SVM model through CV. In other words, while methods such as CV involve finding one SVM model (together with its optimal parameters) that minimises the CV error, Ozogur-Akyuz et al. (In Press) keep all of the models generated during the model selection stage and make predictions according to the model whose hyperplane achieves the maximum separation from a test point. The main advantage of this approach is the computational saving when compared to CV or LOO. However, their method is only applicable to large margin classifiers like SVMs. We continue this line of research, but rather than using the distance of each test point from the hyperplane we explore the idea of using the Nonconformity Measure (Vovk et al., 2005; Shafer & Vovk, 2008) of a test sample to a particular label set. The Nonconformity Measure is a function that evaluates how ‘strange’ a prediction is according to the different possibilities available. The notion of Nonconformity has been proposed in the on-line learning framework of conformal prediction (Shafer & Vovk, 2008), and is a way of scoring how different a new sample is from a bag of old samples. The premise is that if the observed samples are well-sampled then we should have high confidence on correct prediction of new samples, given that they conform to the observations. We take the Nonconformity Measure and apply it to the SVM algorithm during testing in order to gain a time advantage over CV and to generalise the algorithm of Ozogur-Akyuz et al. (In Press). Hence we are not restricted 1A bag is a more general formalism of a mathematical set that allows repeated elements.

Alexander Gammerman - One of the best experts on this subject based on the ideXlab platform.

  • Gammerman: Regression conformal prediction with nearest neighbours
    2013
    Co-Authors: Harris Papadopoulos, Vladimir Vovk, Alexander Gammerman
    Abstract:

    In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical Nonconformity Measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the Nonconformity Measure used by each CP. In effect a Nonconformity Measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel Nonconformity Measures based on the k-Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our Measures with those of the typical regression Measure suggests that a major improvement in terms of predictive region tightness is achieved by the new Measures. 1

  • regression conformal prediction with nearest neighbours
    Journal of Artificial Intelligence Research, 2011
    Co-Authors: Harris Papadopoulos, Vladimir Vovk, Alexander Gammerman
    Abstract:

    In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical Nonconformity Measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the Nonconformity Measure used by each CP. In effect a Nonconformity Measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel Nonconformity Measures based on the k-Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our Measures with those of the typical regression Measure suggests that a major improvement in terms of predictive region tightness is achieved by the new Measures.

  • normalized Nonconformity Measures for regression conformal prediction
    International conference on Artificial intelligence and applications, 2008
    Co-Authors: Harris Papadopoulos, Alexander Gammerman, Volodya Vovk
    Abstract:

    In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose a way of extending the typical Nonconformity Measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. When the regular regression Nonconformity Measure is used the resulting predictive regions have more or less the same width for all examples in the test set. However, it would be more natural for the size of the regions to vary according to how difficult to predict each example is. We define two new Nonconformity Measures, which produce predictive regions of variable width depending on the expected accuracy of the algorithm on each example. As a consequence, the resulting predictive regions are in most cases much tighter than those produced by the simple regression Measure.