Combinación de clasificadores para bioinformática

There are several classification problems in Bioinformatics which are difficult to solve using artificial intelligence techniques because of the diversity of patterns in datasets. In this paper, an ensemble of classifiers is developed to improve the accuracy of classification in bioinformatics datasets. This model is based on the use of different machine learning methods, and it forms clusters to divide the dataset taking into account the performance of the base methods. By means of a meta-classifier, the system learns to decide which classifiers are the best for a given case. In order to compare the new model with some well-known multi-classifiers, eleven international databases are used. It is demonstrated by statistical tests that results of our model are significantly better than those obtained with previous models.

[1]  Fabio Roli,et al.  Multiple classifier systems : Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002 : proceedings , 2002 .

[2]  Xin Yao,et al.  An analysis of diversity measures , 2006, Machine Learning.

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

[4]  Lawrence O. Hall,et al.  Ensemble diversity measures and their application to thinning , 2004, Inf. Fusion.

[5]  C. A. Murthy,et al.  Rough set Based Ensemble Classifier for Web Page Classification , 2006 .

[6]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

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

[8]  Grigorios Tsoumakas,et al.  Ensemble Pruning Using Reinforcement Learning , 2006, SETN.

[9]  C. A. Murthy,et al.  Rough set Based Ensemble Classifier forWeb Page Classification , 2007, Fundam. Informaticae.

[10]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[11]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[12]  Samy Bengio,et al.  Online adaptive policies for ensemble classifiers , 2005, Neurocomputing.

[13]  Anne M. P. Canuto,et al.  Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles , 2007, Pattern Recognit. Lett..

[14]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[15]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[16]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

[17]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[18]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

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

[20]  Hussein A. Abbass,et al.  A novel mixture of experts model based on cooperative coevolution , 2006, Neurocomputing.

[21]  Loris Nanni,et al.  FuzzyBagging: A novel ensemble of classifiers , 2006, Pattern Recognit..

[22]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.