Using Kernel Alignment for Feature Selection in Schizophrenia Diagnostic

Marcella Camargo Mario; Byron Javier; Esp oacute sito Gennaro; Catal agrave Neus; Cort eacute s Ulises; Vi ntilde as Ferran; Tarrag oacute Josep; Emilio Rojo; Nowak Rafal

doi:10.3233/978-1-61499-578-4-143

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Using Kernel Alignment for Feature Selection in Schizophrenia Diagnostic

Marcella Camargo Mario Byron Javier Esp oacute sito Gennaro Catal agrave Neus Cort eacute s Ulises Vi ntilde as Ferran Tarrag oacute Josep Emilio Rojo Nowak Rafal

Year: 2015 Frontiers in artificial intelligence and applications

DOI: 10.3233/978-1-61499-578-4-143

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Abstract

We present a feature selection method for neuroimaging techniques to find objective criteria for diagnosis of schizophrenia. The method is based on kernel alignment with the ideal kernel using Support Vector Machines (SVM) in order to detect relevant features for the diagnostic task.

Keywords:

Schizophrenia (object-oriented programming) Feature selection Artificial intelligence Computer science Kernel (algebra) Pattern recognition (psychology) Selection (genetic algorithm) Psychology Machine learning Mathematics

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Machine Learning in Healthcare

Physical Sciences → Computer Science → Artificial Intelligence

Using Kernel Alignment for Feature Selection in Schizophrenia Diagnostic

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