A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Sensors
Henry Jhoán Areiza-LaverdeGloria M Díaz

Abstract

Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage a...Continue Reading

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Methods Mentioned

BETA
Feature Selection

Software Mentioned

MKL
Pyradiomics
LIBSVM
Breast Imaging Reporting and Data System ( BI - RADS )

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