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Ensemble Generation and Feature Selection for the Identification of Students with Learning Disabilities by Loris Nanni; Alessandra Lumini is a Computer Science article available to read on EtoBox.

What is Ensemble Generation and Feature Selection for the Identification of Students with Learning Disabilities about?

In this paper, we have made an extensive study of artificial intelligence (AI) techniques like ensemble of classifiers and feature selection for the identification of students with learning disabilities. The experimental results show that our best method, which combines both ensemble of classifiers and feature selection, can correctly identify up to 50% of the learning disabilities (LD) students with 100% confidence. Also when predicting samples in ''junior high school" using model built on the ''elementary school" students and when the ''junior high school" samples are used to build the model we predict the samples in the ''elementary school" dataset. In particular, we propose variants of two recent Feature Transform-based ensemble methods (Rotation Forest and Input Decimated Ensemble). In the Rotation Forest, the feature set is randomly split into subsets and Principal Component Analysis (PCA) is used to transform the features that belong to a subset. The Input Decimated Ensemble first singles out a given class i and runs PCA on this data only. This transformation is applied to the whole dataset and a classifier D i is trained using these transformed patterns. This choice limits

Who reads Ensemble Generation and Feature Selection for the Identification of Students with Learning Disabilities?

Author

Loris Nanni; Alessandra Lumini

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0957-4174)

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)