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A novel divide-and-merge classification for high dimensional datasets

Paper ID Volume ID Publish Year Pages File Format Full-Text
15169 1384 2013 12 PDF Available
Title
A novel divide-and-merge classification for high dimensional datasets
Abstract

High dimensional datasets contain up to thousands of features, and can result in immense computational costs for classification tasks. Therefore, these datasets need a feature selection step before the classification process. The main idea behind feature selection is to choose a useful subset of features to significantly improve the comprehensibility of a classifier and maximize the performance of a classification algorithm. In this paper, we propose a one-per-class model for high dimensional datasets. In the proposed method, we extract different feature subsets for each class in a dataset and apply the classification process on the multiple feature subsets. Finally, we merge the prediction results of the feature subsets and determine the final class label of an unknown instance data. The originality of the proposed model is to use appropriate feature subsets for each class. To show the usefulness of the proposed approach, we have developed an application method following the proposed model. From our results, we confirm that our method produces higher classification accuracy than previous novel feature selection and classification methods.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► High dimensional dataset is difficult for classification analysis. ► Transform multiclass problem to multiple binary-class problem is well known method. ► In this study, we attempted to apply feature selection to above method. ► Our strategy is to select different proper features for each class.

Keywords
Feature selection; Classification; Data preprocessing; One-per-class
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A novel divide-and-merge classification for high dimensional datasets
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 42, February 2013, Pages 23–34
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us