Authors:
Diego Rego-Fernández
;
Verónica Bolón-Canedo
and
Amparo Alonso-Betanzos
Affiliation:
University of A Coruña, Spain
Keyword(s):
Feature Selection, Microarray, Machine Learning, Scalability.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
Lately, derived from the Big Data problem, researchers in Machine Learning became also interested not only
in accuracy, but also in scalability. Although scalability of learning methods is a trending issue, scalability of
feature selection methods has not received the same amount of attention. In this research, an attempt to study
scalability of both Feature Selection and Machine Learning on microarray datasets will be done. For this sake,
the minimum redundancy maximum relevance (mRMR) filter method has been chosen, since it claims to be
very adequate for this type of datasets. Three synthetic databases which reflect the problematics of microarray
will be evaluated with new measures, based not only in an accurate selection but also in execution time. The
results obtained are presented and discussed.