Semi-supervised Multiple Classifier Systems: Background and Research Directions

Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper's goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research directions.

[1]  Naonori Ueda,et al.  Exploitation of Unlabeled Sequences in Hidden Markov Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[3]  Ana L. N. Fred,et al.  Finding Consistent Clusters in Data Partitions , 2001, Multiple Classifier Systems.

[4]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[5]  George Nagy,et al.  Self-corrective character recognition system , 1966, IEEE Trans. Inf. Theory.

[6]  Olac Fuentes,et al.  Face Recognition Using Unlabeled Data , 2003, Computación y Sistemas.

[7]  Fabio Roli,et al.  The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement , 2003, Multiple Classifier Systems.

[8]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Christophe Ambroise,et al.  Semi-supervised MarginBoost , 2001, NIPS.

[10]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[11]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[12]  George Nagy DocLab.  Classifiers That Improve with Use , 2004 .

[13]  Salvatore J. Stolfo,et al.  Data mining-based intrusion detectors: an overview of the columbia IDS project , 2001, SGMD.

[14]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[15]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[18]  Joydeep Ghosh,et al.  Design and control of large collections of learning agents , 2003 .

[19]  Vittorio Castelli,et al.  On the exponential value of labeled samples , 1995, Pattern Recognit. Lett..

[20]  Ayhan Demiriz,et al.  Exploiting unlabeled data in ensemble methods , 2002, KDD.

[21]  David J. Miller,et al.  A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data , 1996, NIPS.

[22]  Tzay Y. Young,et al.  On decision-directed estimation and stochastic approximation (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[23]  Kagan Tumer,et al.  Optimal Payoff Functions for Members of Collectives , 2001, Adv. Complex Syst..

[24]  Raymond J. Mooney,et al.  Diverse ensembles for active learning , 2004, ICML.

[25]  Joydeep Ghosh,et al.  Multiclassifier Systems: Back to the Future , 2002, Multiple Classifier Systems.

[26]  Ludmila I. Kuncheva,et al.  Classifier Ensembles for Changing Environments , 2004, Multiple Classifier Systems.

[27]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[28]  Alexander H. Waibel,et al.  Unsupervised training of a speech recognizer: recent experiments , 1999, EUROSPEECH.

[29]  Johannes R. Sveinsson,et al.  Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data , 2001, Multiple Classifier Systems.

[30]  Fabio Gagliardi Cozman,et al.  Semi-supervised Learning of Classifiers : Theory , Algorithms and Their Application to Human-Computer Interaction , 2004 .

[31]  Sarunas Raudys,et al.  Experts' Boasting in Trainable Fusion Rules , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Nicu Sebe,et al.  Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Qiong Jackson,et al.  An adaptive classifier design for high-dimensional data analysis with a limited training data set , 2001, IEEE Trans. Geosci. Remote. Sens..

[34]  Michael I. Jordan,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.