A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

In area of bioinformatics, large amount of data is being harvested with functional and genetic features of proteins. The data is being generated consists of thousands of features with least observations instances. In such case, we need computational tools to analyze and extract useful information from vast amount of raw data which help in predicting the major biological functions of genes and proteins with respect to their structural behavior. Thus, in this study, we use a new hybrid approach for features selection and classifying data using Support Vector Machine (SVM) classifiers with Quadratic Discriminant Analysis (QDA) as generative classifiers to increase more performance and accuracy. We compare our results with previous results and seem to be much promising. The proposed method provides the higher recognition ratio rather than other method used in previous studies. The obtained results are also compared with other different classifiers and our hybrid classifiers give more accuracy and achieve better results than any other classifiers
Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition
Subtitle of host publicationLecture Notes in Computer Science
EditorsPetra Perner
Place of PublicationBerlin
PublisherSpringer
Pages660-668
Number of pages9
Volume7376
ISBN (Electronic)9783642315374
ISBN (Print)9783642315367
DOIs
Publication statusPublished - 2012
Event8th International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012 - Berlin, Berlin, Germany
Duration: 13 Jul 201220 Jul 2012

Conference

Conference8th International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012
Abbreviated titleMLDM 2012
CountryGermany
CityBerlin
Period13/07/1220/07/12

Fingerprint

Protein folding
Support vector machines
Classifiers
Proteins
Discriminant analysis
Bioinformatics
Feature extraction
Genes

Cite this

Singh, L., Chetty, G., & Sharma, D. (2012). A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Computer Science (Vol. 7376, pp. 660-668). Berlin: Springer. https://doi.org/10.1007/978-3-642-31537-4_51
Singh, Lavneet ; Chetty, Girija ; Sharma, Dharmendra. / A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Computer Science. editor / Petra Perner. Vol. 7376 Berlin : Springer, 2012. pp. 660-668
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title = "A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines",
abstract = "In area of bioinformatics, large amount of data is being harvested with functional and genetic features of proteins. The data is being generated consists of thousands of features with least observations instances. In such case, we need computational tools to analyze and extract useful information from vast amount of raw data which help in predicting the major biological functions of genes and proteins with respect to their structural behavior. Thus, in this study, we use a new hybrid approach for features selection and classifying data using Support Vector Machine (SVM) classifiers with Quadratic Discriminant Analysis (QDA) as generative classifiers to increase more performance and accuracy. We compare our results with previous results and seem to be much promising. The proposed method provides the higher recognition ratio rather than other method used in previous studies. The obtained results are also compared with other different classifiers and our hybrid classifiers give more accuracy and achieve better results than any other classifiers",
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Singh, L, Chetty, G & Sharma, D 2012, A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. in P Perner (ed.), Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Computer Science. vol. 7376, Springer, Berlin, pp. 660-668, 8th International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012, Berlin, Germany, 13/07/12. https://doi.org/10.1007/978-3-642-31537-4_51

A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. / Singh, Lavneet; Chetty, Girija; Sharma, Dharmendra.

Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Computer Science. ed. / Petra Perner. Vol. 7376 Berlin : Springer, 2012. p. 660-668.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AB - In area of bioinformatics, large amount of data is being harvested with functional and genetic features of proteins. The data is being generated consists of thousands of features with least observations instances. In such case, we need computational tools to analyze and extract useful information from vast amount of raw data which help in predicting the major biological functions of genes and proteins with respect to their structural behavior. Thus, in this study, we use a new hybrid approach for features selection and classifying data using Support Vector Machine (SVM) classifiers with Quadratic Discriminant Analysis (QDA) as generative classifiers to increase more performance and accuracy. We compare our results with previous results and seem to be much promising. The proposed method provides the higher recognition ratio rather than other method used in previous studies. The obtained results are also compared with other different classifiers and our hybrid classifiers give more accuracy and achieve better results than any other classifiers

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Singh L, Chetty G, Sharma D. A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. In Perner P, editor, Machine Learning and Data Mining in Pattern Recognition: Lecture Notes in Computer Science. Vol. 7376. Berlin: Springer. 2012. p. 660-668 https://doi.org/10.1007/978-3-642-31537-4_51