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.
TopBackground
The common practice in such increasingly important bioinformatics field is to employ a range of accessible methodologies that can be broadly classified into three categories:
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Classification methods based on global gene expression analysis (Golub et al., 1999; Alizadeh et al., 2000; Ross et al., 2000) specifically aimed at applying a single technique to a specific gene expression dataset;
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Traditional statistical approaches such as Principal Component Analysis (Liberati et al., 2005; Garatti et al., 2007), discriminant analysis (Nguyen and Rocke, 2002) or Bayesian decision theory (Bosin et al., 2006);
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Machine learning techniques such as neural (Tung and Quek, 2005; Khan et al., 2001) and logical (Muselli and Liberati, 2002) networks, decision trees and Support Vector Machines (SVM) (Guyon et al., 2002; Furey et al., 2001; Valentini, 2002).
Nonetheless, (Statnikov et al., 2005) reported that such results lack a consistent and systematic approach as they validate their methods differently, on different public datasets and on different limited sets of features. Dudoit (2002) and colleagues have compared the performance of various micro-array data classification methods, and a recent extensive comparison (Lee et al., 2005) provides some additional insights. The relevance of good feature selection methods has been discussed by Guyon (2002) and colleagues with special emphasis on over-fitting, but the recommendations in literature do not give evidence for a single best method for either the classification of micro-array data, or at least their feature selection. It has also been pointed out (Tung and Quek, 2005) that often classifiers work as black boxes, the decision making process being not intuitive to the human cognitive process and, more importantly, the knowledge extracted by these classifiers from the numerical training not being easy to be understood and then assessed.
Key Terms in this Chapter
Principal Component Analysis: Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance.
Gene: Sentence in the genetic alphabet codifying a cell instruction.
Minimum Description Length: Information theory principle claiming optimality for the more oeconomical description of both the model and coding fully describing the process from where the data samples are exctracted.
Multitarget Classification: Partition of the set of samples in more than two classes.
Micro-Array: Bio-assay technology allowing to measure the expression of thousands of genes from a sample on a single chip.
Lymphoblastic Leukemia: Class of blood cancers quite diffuse in children.
Bio-Informatics: The processing of the huge amount of information pertaining biology.