Novel hierarchical feature selection algorithms for predicting genes' aging-related function

Abstract

Hierarchical Feature Selection (HFS) is an under-explored subarea of machine learning/ data mining. Unlike conventional (flat) feature selection algorithms, HFS algorithms work by exploiting hierarchical (generalizationspecialization) relationships between features, in order to improve the predictive accuracy of classifiers. The basic idea is to remove hierarchical redundancy between features, where the presence of a feature in an instance implies the presence of all ancestors of that feature in that instance. By using an HFS algorithm to select a feature subset where the hierarchical redundancy among features is eliminated or reduced, and then giving only the selected feature subset to a classification algorithm, it is possible to improve the predictive accuracy of classification algorithms. In terms of applications, this thesis focuses on datasets of aging-related genes. This type of dataset is an interesting type of application for machine learning/data mining methods due to the technical difficulty and ethical issues associated with doing aging experiments with humans and the strategic importance of research on the biology of aging, since old age is the greatest risk factor for a number of diseases, but is still a not well understood biological process.