Exploring Attribute Selection in Hierarchical Classification

In the domain of many classification problems, classes have relations of dependency that are represented in hierarchical structures. These problems are known as hierarchical classification problems. Methods based on different approaches, considering hierarchical relations in different ways, have been proposed to solve them, in the attempt to achieve better predictive performance. In this work, we explore attribute selection techniques in conjunction with hierarchical classifiers from different categories, with the goal of improving their respective performances. Computational experiments, made with 18 hierarchical datasets, have indicated that the adopted classifiers attain better predictive accuracy when the most relevant attributes are considered in their construction.

[1]  Isabelle Guyon,et al.  An Introduction to Feature Extraction , 2006, Feature Extraction.

[2]  Amanda Clare,et al.  Predicting gene function in Saccharomyces cerevisiae , 2003, ECCB.

[3]  Ee-Peng Lim,et al.  Hierarchical text classification and evaluation , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[4]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[5]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[6]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[7]  Stan Matwin,et al.  Functional Annotation of Genes Using Hierarchical Text Categorization , 2005 .

[8]  Susan T. Dumais,et al.  Hierarchical classification of Web content , 2000, SIGIR '00.

[9]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[10]  Huan Liu,et al.  A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.

[11]  Alex Alves Freitas,et al.  Lazy attribute selection: Choosing attributes at classification time , 2011, Intell. Data Anal..

[12]  Alex Alves Freitas,et al.  A hybrid PSO/ACO algorithm for classification , 2007, GECCO '07.

[13]  Christopher DeCoro,et al.  Hierarchical Shape Classification Using Bayesian Aggregation , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[14]  Alex Alves Freitas,et al.  Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers , 2009 .

[15]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[16]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[17]  Alex Alves Freitas,et al.  Top-Down Hierarchical Ensembles of Classifiers for Predicting G-Protein-Coupled-Receptor Functions , 2008, BSB.

[18]  Alex A. Freitas,et al.  A survey of hierarchical classification across different application domains , 2010, Data Mining and Knowledge Discovery.

[19]  Alex Alves Freitas,et al.  Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation , 2008, Soft Comput..

[20]  Alex Alves Freitas,et al.  Improving Local Per Level Hierarchical Classification , 2012, J. Inf. Data Manag..