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VPMCD: Variable interaction modeling approach for class discrimination in biological systems
Author(s) -
Raghuraj Rao,
Lakshminarayanan Samavedham
Publication year - 2007
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2007.01.052
Subject(s) - robustness (evolution) , computer science , class (philosophy) , benchmark (surveying) , machine learning , artificial intelligence , data mining , biological data , stability (learning theory) , variable (mathematics) , pattern recognition (psychology) , algorithm , mathematics , bioinformatics , mathematical analysis , biochemistry , chemistry , geodesy , biology , gene , geography
Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter‐relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.