The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Minho Lee - One of the best experts on this subject based on the ideXlab platform.
-
deep learning of support vector machines with Class Probability output networks
Neural Networks, 2015Co-Authors: Sangwook Kim, Rhee Man Kil, Minho LeeAbstract:Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with Class Probability output networks (CPONs) to provide better generalization power for pattern Classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM Classifiers with CPONs. The proposed structure closely approaches the ideal Bayes Classifier as the number of layers increases. Using a simulation of Classification problems, the effectiveness of the proposed method is demonstrated.
Rhee Man Kil - One of the best experts on this subject based on the ideXlab platform.
-
Classifying heart conditions based on Class Probability output networks
Neurocomputing, 2019Co-Authors: Han Bin Bae, Rhee Man Kil, Min Seop Park, Hee Yong YounAbstract:Abstract This paper presents a novel method of Classifying heart conditions from an electrocardiography (ECG) signal. For this purpose, the R-R intervals of ECG signal are analyzed by Gamma distribution parameters and Classified into normal (NR) or abnormal (AN) ECG waves. For the normal ECG waves, the heart condition is further investigated by analyzing the dynamic behavior of heart activity based on the correlation between successive R-R intervals and long-term analysis. The Classification of heart conditions is made by estimating the conditional Class probabilities using Class Probability output networks (CPONs). The simulation for Classifying heart conditions using the MIT-BIH data sets reveals that the proposed approach is effective for Classifying heart conditions and allows more accurate Classification than the existing Classifiers such as the k-NN and SVM.
-
Classification of the trained and untrained emitter types based on Class Probability output networks
Neurocomputing, 2017Co-Authors: Lee Suk Kim, Han Bin Bae, Rhee Man KilAbstract:Modern airplanes and ships are equipped with radars emitting specific patterns of electromagnetic signals. The radar antennas are detecting these patterns which are required to identify the types of emitters. A conventional way of emitter identification is to categorize the radar patterns according to the sequences of radar frequencies, differences in time of arrivals, and pulse widths of emitting signals by human experts. In this respect, this paper proposes a method of Classifying the radar patterns automatically using the network of calculating the p-values for testing the hypotheses of the types of emitters referred to as the Class Probability output network (CPON). The proposed method also provides a new way of identifying the trained and untrained emitter types. Through the simulation for radar pattern Classification, the effectiveness of the proposed approach has been demonstrated.
-
deep learning of support vector machines with Class Probability output networks
Neural Networks, 2015Co-Authors: Sangwook Kim, Rhee Man Kil, Minho LeeAbstract:Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with Class Probability output networks (CPONs) to provide better generalization power for pattern Classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM Classifiers with CPONs. The proposed structure closely approaches the ideal Bayes Classifier as the number of layers increases. Using a simulation of Classification problems, the effectiveness of the proposed method is demonstrated.
-
automatic media data rating based on Class Probability output networks
IEEE Transactions on Consumer Electronics, 2010Co-Authors: Harvey Rosas, Rhee Man Kil, Seungwan HanAbstract:This paper presents a novel method of Classifying media data whether they include X-rated contents or not. In our work, the Classification of media data is performed using the Class Probability output network (CPON) which estimates the conditional Class Probability. Consequently, the Classification of media data can be done using the degree of confidence for the Class membership, not just using the discriminant value which is usually used in many Classification problems. Furthermore, the accuracy of the estimated conditional Class Probability can be measured in the suggested CPON and this gives a good guideline for the final decision of Classification. To demonstrate the effectiveness of the suggested method, the simulation for automatic media rating of the data sampled from multimedia data streams in the Internet was performed. We showed that the suggested CPON-based method provides the better performance than other Classifiers using discriminant functions.
-
pattern Classification with Class Probability output network
IEEE Transactions on Neural Networks, 2009Co-Authors: Woon Jeung Park, Rhee Man KilAbstract:The output of a Classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily represent the posterior Probability for the soft decision of Classification. In this context, it is desirable that the output of a Classifier be calibrated in such a way to give the meaning of the posterior Probability of Class membership. This paper presents a new method of postprocessing for the probabilistic scaling of Classifier's output. For this purpose, the output of a Classifier is analyzed and the distribution of the output is described by the beta distribution parameters. For more accurate approximation of Class output distribution, the beta distribution parameters as well as the kernel parameters describing the discriminant function are adjusted in such a way to improve the uniformity of beta cumulative distribution function (CDF) values for the given Class output samples. As a result, the Classifier with the proposed scaling method referred to as the Class Probability output network (CPON) can provide accurate posterior probabilities for the soft decision of Classification. To show the effectiveness of the proposed method, the simulation for pattern Classification using the support vector machine (SVM) Classifiers is performed for the University of California at Irvine (UCI) data sets. The simulation results using the SVM Classifiers with the proposed CPON demonstrated a statistically meaningful performance improvement over the SVM and SVM-related Classifiers, and also other probabilistic scaling methods.
Shivani Agarwal - One of the best experts on this subject based on the ideXlab platform.
-
on the statistical consistency of plug in Classifiers for non decomposable performance measures
Neural Information Processing Systems, 2014Co-Authors: Harikrishna Narasimhan, Rohit Vaish, Shivani AgarwalAbstract:We study consistency properties of algorithms for non-decomposable performance measures that cannot be expressed as a sum of losses on individual data points, such as the F-measure used in text retrieval and several other performance measures used in Class imbalanced settings. While there has been much work on designing algorithms for such performance measures, there is limited understanding of the theoretical properties of these algorithms. Recently, Ye et al. (2012) showed consistency results for two algorithms that optimize the F-measure, but their results apply only to an idealized setting, where precise knowledge of the underlying Probability distribution (in the form of the 'true' posterior Class Probability) is available to a learning algorithm. In this work, we consider plug-in algorithms that learn a Classifier by applying an empirically determined threshold to a suitable 'estimate' of the Class Probability, and provide a general methodology to show consistency of these methods for any non-decomposable measure that can be expressed as a continuous function of true positive rate (TPR) and true negative rate (TNR), and for which the Bayes optimal Classifier is the Class Probability function thresholded suitably. We use this template to derive consistency results for plug-in algorithms for the F-measure and for the geometric mean of TPR and precision; to our knowledge, these are the first such results for these measures. In addition, for continuous distributions, we show consistency of plug-in algorithms for any performance measure that is a continuous and monotonically increasing function of TPR and TNR. Experimental results confirm our theoretical findings.
-
on the consistency of output code based learning algorithms for multiClass learning problems
Conference on Learning Theory, 2014Co-Authors: Harish G Ramaswamy, Shivani Agarwal, Balaji Srinivasan Babu, Robert C. WilliamsonAbstract:A popular approach to solving multiClass learning problems is to reduce them to a set of binary Classification problems through some output code matrix: the widely used one-vs-all and all-pairs methods, and the error-correcting output code methods of Dietterich and Bakiri (1995), can all be viewed as special cases of this approach. In this paper, we consider the question of statistical consistency of such methods. We focus on settings where the binary problems are solved by minimizing a binary surrogate loss, and derive general conditions on the binary surrogate loss under which the one-vs-all and all-pairs code matrices yield consistent algorithms with respect to the multiClass 0-1 loss. We then consider general multiClass learning problems defined by a general multiClass loss, and derive conditions on the output code matrix and binary surrogates under which the resulting algorithm is consistent with respect to the target multiClass loss. We also consider probabilistic code matrices, where one reduces a multiClass problem to a set of Class Probability labeled binary problems, and show that these can yield benefits in the sense of requiring a smaller number of binary problems to achieve overall consistency. Our analysis makes interesting connections with the theory of proper composite losses (Buja et al., 2005; Reid and Williamson, 2010); these play a role in constructing the right ‘decoding’ for converting the predictions on the binary problems to the final multiClass prediction. To our knowledge, this is the first work that comprehensively studies consistency properties of output code based methods for multiClass learning.
-
on the relationship between binary Classification bipartite ranking and binary Class Probability estimation
Neural Information Processing Systems, 2013Co-Authors: Harikrishna Narasimhan, Shivani AgarwalAbstract:We investigate the relationship between three fundamental problems in machine learning: binary Classification, bipartite ranking, and binary Class Probability estimation (CPE). It is known that a good binary CPE model can be used to obtain a good binary Classification model (by thresholding at 0.5), and also to obtain a good bipartite ranking model (by using the CPE model directly as a ranking model); it is also known that a binary Classification model does not necessarily yield a CPE model. However, not much is known about other directions. Formally, these relationships involve regret transfer bounds. In this paper, we introduce the notion of weak regret transfer bounds, where the mapping needed to transform a model from one problem to another depends on the underlying Probability distribution (and in practice, must be estimated from data). We then show that, in this weaker sense, a good bipartite ranking model can be used to construct a good Classification model (by thresholding at a suitable point), and more surprisingly, also to construct a good binary CPE model (by calibrating the scores of the ranking model).
-
on the statistical consistency of algorithms for binary Classification under Class imbalance
International Conference on Machine Learning, 2013Co-Authors: Aditya Krishna Menon, Shivani Agarwal, Harikrishna Narasimhan, Sanjay ChawlaAbstract:Class imbalance situations, where one Class is rare compared to the other, arise frequently in machine learning applications. It is well known that the usual misClassification error is ill-suited for measuring performance in such settings. A wide range of performance measures have been proposed for this problem. However, despite the large number of studies on this problem, little is understood about the statistical consistency of the algorithms proposed with respect to the performance measures of interest. In this paper, we study consistency with respect to one such performance measure, namely the arithmetic mean of the true positive and true negative rates (AM), and establish that some practically popular approaches, such as applying an empirically determined threshold to a suitable Class Probability estimate or performing an empirically balanced form of risk minimization, are in fact consistent with respect to the AM (under mild conditions on the underlying distribution). Experimental results confirm our consistency theorems.
Gert Cauwenberghs - One of the best experts on this subject based on the ideXlab platform.
-
gini support vector machine quadratic entropy based robust multi Class Probability regression
Journal of Machine Learning Research, 2007Co-Authors: Shantanu Chakrabartty, Gert CauwenberghsAbstract:Many Classification tasks require estimation of output Class probabilities for use as confidence scores or for inference integrated with other models. Probability estimates derived from large margin Classifiers such as support vector machines (SVMs) are often unreliable. We extend SVM large margin Classification to GiniSVM maximum entropy multi-Class Probability regression. GiniSVM combines a quadratic (Gini-Simpson) entropy based agnostic model with a kernel based similarity model. A form of Huber loss in the GiniSVM primal formulation elucidates a connection to robust estimation, further corroborated by the impulsive noise filtering property of the reverse water-filling procedure to arrive at normalized Classification margins. The GiniSVM normalized Classification margins directly provide estimates of Class conditional probabilities, approximating kernel logistic regression (KLR) but at reduced computational cost. As with other SVMs, GiniSVM produces a sparse kernel expansion and is trained by solving a quadratic program under linear constraints. GiniSVM training is efficiently implemented by sequential minimum optimization or by growth transformation on Probability functions. Results on synthetic and benchmark data, including speaker verification and face detection data, show improved Classification performance and increased tolerance to imprecision over soft-margin SVM and KLR.
Issei Sato - One of the best experts on this subject based on the ideXlab platform.
-
diagnostic uncertainty calibration towards reliable machine predictions in medical domain
arXiv: Machine Learning, 2020Co-Authors: Takahiro Mimori, Keiko Sasada, Hirotaka Matsui, Issei SatoAbstract:Label disagreement between human experts is a common issue in the medical domain and poses unique challenges in the evaluation and learning of Classification models. In this work, we extend metrics for Probability prediction, including calibration, i.e., the reliability of predictive Probability, to adapt to such a situation. We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over Class Probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.