Showing papers on "Classifier chains published in 2016"

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.

Abstract: Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient’s life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied. 662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains. In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models. The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.

CHADE: Metalearning with Classifier Chains for Dynamic Combination of Classifiers

Abstract: Dynamic selection or combination DSC methods allow to select one or more classifiers from an ensemble according to the characteristics of a given test instance x. Most methods proposed for this purpose are based on the nearest neighbours algorithm: it is assumed that if a classifier performed well on a set of instances similar to x, it will also perform well on x. We address the problem of dynamically combining a pool of classifiers by combining two approaches: metalearning and multi-label classification. Taking into account that diversity is a fundamental concept in ensemble learning and the interdependencies between the classifiers cannot be ignored, we solve the multi-label classification problem by using a widely known technique: Classifier Chains CC. Additionally, we extend a typical metalearning approach by combining metafeatures characterizing the interdependencies between the classifiers with the base-level features. We executed experiments on 42 classification datasets and compared our method with several state-of-the-art DSC techniques, including another metalearning approach. Results show that our method allows an improvement over the other metalearning approach and is very competitive with the other four DSC methods.

Enhancing multi-label classification based on local label constraints and classifier chains

Abstract: In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.

Ordered Classifier Chains for Multi-label Classification

Abstract: Classifier chains method is introduced recently in multi-label classification scope as a high predictive performance technique aims to exploit label dependencies and in the meantime preserving the computational complexity in a desirable level. In this paper, we present a method for chain's order, called Ordered Classifier Chains (OCC), elaborating that the sequence of labels in the chain plays an important role in predictive performance of corresponding multi-label classifiers. OCC proposes making use of correlation of every class label with that of features. OCC renders an ordering of class labels in their descending order. Once the ordering of labels is determined, the features along with every label are fed to binary classifier. In the classifier chain model the feature space of every binary classifier is extended with the new order of labels. In order to specify association of each sample with the set of class labels, it is given to all of classifiers. Empirical evaluations include an extensive range of multi-label datasets reveal that OCC manages to improve the classification performance compared to existing approaches.

Asymptotic consistency and order specification for logistic classifier chains in multi-label learning

Abstract: Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in which the conditional probabilities are of the logistic form. In particular we find conditions on the number of labels and the distribution of feature vector under which the estimated mode of the joint distribution of labels converges to the true mode. Best of our knowledge, this important issue has not yet been studied in the context of multi-label learning. We also investigate how the order of model building in a chain influences the estimation of the joint distribution of labels. We establish the link between the problem of incorrect ordering in the chain and incorrect model specification. We propose a procedure of determining the optimal ordering of labels in the chain, which is based on using measures of correct specification and allows to find the ordering such that the consecutive logistic models are best possibly specified. The other important question raised in this paper is how accurately can we estimate the joint posterior probability when the ordering of labels is wrong or the logistic models in the chain are incorrectly specified. The numerical experiments illustrate the theoretical results.

L-classifier chains classification and variable selection for multi-label datasets

Abstract: ...................................................................................................................... iii List of figures ............................................................................................................. vii List of tables................................................................................................................ ix List of appendices ....................................................................................................... xi List of abbreviations and/or acronyms ................................................................... xii CHAPTER 1: Introduction ........................................................................................ 1 1.1 Background ................................................................................................... 1 1.2 Notation ......................................................................................................... 2 1.3 Overview ....................................................................................................... 2 CHAPTER 2: Multi-label classification .................................................................... 4 2.1 Classification hierarchy ................................................................................ 4 2.2 Complexity of multi-label datasets ............................................................... 6 2.3 Objectives when analysing multi-label datasets ........................................... 7 2.4 Label dependence .......................................................................................... 8 2.5 Multi-label evaluation measures ................................................................. 10 2.6 Different approaches to multi-label classification ...................................... 13 2.7 Probem transformation methods ................................................................. 14 2.7.1 Binary relevance ................................................................................. 14 2.7.2 Label powerset .................................................................................... 15 2.7.3 Pairwise methods ................................................................................ 17 CHAPTER 3: Classifier chains in multi-label classification................................. 19 3.1 Classifier chains .......................................................................................... 19 3.2 Modifications of classifier chains ............................................................... 22 3.2.1 Ensemble of classifier chains .............................................................. 23 3.2.2 1-Classifier chains .............................................................................. 23 3.2.3 Limitations of the classifier chains-based methods ............................ 28 3.3 Lclassifier chains ...................................................................................... 30 Stellenbosch University https://scholar.sun.ac.za