Multitarget Classifiers for Mining in Bioinformatics

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Author(s): Diego Liberati (Istituto di Elettronica e Ingegneria dell’Informazione e delle Telecomunicazioni Consiglio Nazionale delle Ricerche Politecnico di Milano, Italy)
Copyright: 2009
Pages: 10
Source title: Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch038

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Building effective multitarget classifiers is still an on-going research issue: this chapter proposes the use of the knowledge gleaned from a human expert as a practical way for decomposing and extend the proposed binary strategy. The core is a greedy feature selection approach that can be used in conjunction with different classification algorithms, leading to a feature selection process working independently from any classifier that could then be used. The procedure takes advantage from the Minimum Description Length principle for selecting features and promoting accuracy of multitarget classifiers. Its effectiveness is asserted by experiments, with different state-of-the-art classification algorithms such as Bayesian and Support Vector Machine classifiers, over dataset publicly available on the Web: gene expression data from DNA micro-arrays are selected as a paradigmatic example, containing a lot of redundant features due to the large number of monitored genes and the small cardinality of samples. Therefore, in analysing these data, like in text mining, a major challenge is the definition of a feature selection procedure that highlights the most relevant genes in order to improve automatic diagnostic classification.

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Multitarget Classifiers for Mining in Bioinformatics

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