NDLI: Multivariate filter and PSO in protein function classification

Content Provider	IEEE Xplore Digital Library
Author	Abdul-Rahman, S. Mohamed-Hussein, Z.-A. Bakar, A.A.
Copyright Year	2010
Description	Author affiliation: Center for Artificial Intelligence, Technology (CAIT), Faculty of Information Science & Technology, 43600 UKM Bangi, Selangor, Malaysia (Abdul-Rahman, S.; Bakar, A.A.) \|\| School of Biosciences & Biotechnology, Faculty of Science & Technology, 43600 UKM Bangi, Selangor, Malaysia (Mohamed-Hussein, Z.-A.)
Abstract	Protein features are often complex, and they are challenging to classify. In identifying the most discriminatory features in protein sequences, we propose a new feature-selection strategy by integrating the multivariate filter and Particle Swarm Optimisation (PSO) algorithms. Experimental results, based on the number of reducts and classification accuracy, were analysed in both the filter and wrapper phases. For our dataset, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets. In the filter phase, the accuracy is improved more than 4% in three out of four multivariate feature selection methods compared to a model without feature selection. In the second phase, the accuracy is increased from 97.51% to 100%. We also demonstrate the importance of the correct parameter settings in the PSO to guarantee good performance.
Starting Page	330
Ending Page	333
File Size	155883
Page Count	4
File Format	PDF
ISBN	9781424478972
e-ISBN	9781424478965
DOI	10.1109/SOCPAR.2010.5686158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-07
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Feature Selection Correlation Accuracy Multivariate Filter Filtering algorithms Particle Swarm Optimisation Entropy Classification algorithms Protein Sequences Optimization
Content Type	Text
Resource Type	Article

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