Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.

[1]  Derek Partridge,et al.  Multiple Classifier Systems: Software Engineered, Automatically Modular Leading to a Taxonomic Overview , 2002, Pattern Analysis & Applications.

[2]  Ian Witten,et al.  Data Mining , 2000 .

[3]  Efstathios Stamatatos,et al.  Music Performer Recognition Using an Ensemble of Simple Classifiers , 2002, ECAI.

[4]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[5]  David W. Opitz,et al.  Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.

[6]  Robert P. W. Duin,et al.  The combining classifier: to train or not to train? , 2002, Object recognition supported by user interaction for service robots.

[7]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

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

[9]  José Manuel Iñesta Quereda,et al.  Statistical description models for melody analysis and characterization , 2004, ICMC.

[10]  Jorge Calera-Rubio,et al.  Style recognition through statistical event models , 2005 .

[11]  Ludmila I. Kuncheva,et al.  That Elusive Diversity in Classifier Ensembles , 2003, IbPRIA.

[12]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[13]  Thomas G. Dietterich.  Machine Learning for Sequential Data: A Review , 2002, SSPR/SPR.

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