NDLI: Multiclass Fuzzy Clustering Support Vector Machines for Protein Local Structure Prediction

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhong Jieyue He Yi Pan
Copyright Year	2007
Description	Author affiliation: Univ. of South Carolina, Spartanburg (Wei Zhong)
Abstract	Local protein structure prediction is a central task in bioinformatics research. Local protein structure prediction can be transformed into the multiclass problem for huge datasets. In previous study, multiclass clustering support vector machines (CSVMs) was proposed for local protein structure prediction. The greedy algorithm is utilized to select the next closest class if CSVM modeled for the assigned class predicts the sequence segment as negative. However, the greedy algorithm may not be optimal. If all CSVM predict the sequence segment as negative, this sequence segment cannot be classified. In order to further improve performance of the multiclass problem, we propose fuzzy clustering support vector machines (FCSVMs) in this study. The FCSVMs model calculates the class membership value of the given sequence segment for each class and assigns the representative structure of the finally selected class to the sequence segment. Values of the fuzzy membership function are based on testing accuracy of decision function outputs from FCSVMs. Under this mechanism, values of different fuzzy membership functions can be compared. FCSVMs are built specifically for each class partitioned intelligently by the clustering algorithm. This feature makes learning tasks for each FCSVM more specific and simpler. Furthermore, FCSVM modeled for each class can be easily parallelized to handle the complex multiclass problems for huge datasets. Using fuzzy membership functions, all sequence segments can be classified. Compared with the conventional clustering algorithm and CSVMs, testing accuracy for local structure prediction has been improved noticeably when the FCSVMs model is applied.
Starting Page	21
Ending Page	26
File Size	202454
Page Count	6
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Clustering algorithms Support vector machine classification Partitioning algorithms Classification tree analysis Greedy algorithms Predictive models Protein engineering Computer science Testing fuzzy membership funcion support vector machine local protein structure prediction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Binary classification tree for multiclass classification with observation-based clustering

Tree Decomposition of Multiclass Problems

A clustering based adaptive DAG for multiclass Support Vector Machine

Towards Optimal Discriminating Order for Multiclass Classification

A new framework for an adaptive classifier model

G Protein-Coupled Receptor Classification at the Subfamily Level with Probabilistic Suffix Tree

Using Support Vector Machines for Numerical Prediction

Two novel methods for multiclass ECG arrhythmias classification based on PCA, fuzzy support vector machine and unbalanced clustering

Improved Fuzzy Multicategory Support Vector Machines Classifier

Multiclass Fuzzy Clustering Support Vector Machines for Protein Local Structure Prediction

Similar Documents

Binary classification tree for multiclass classification with observation-based clustering

Tree Decomposition of Multiclass Problems

A clustering based adaptive DAG for multiclass Support Vector Machine

Towards Optimal Discriminating Order for Multiclass Classification

A new framework for an adaptive classifier model

G Protein-Coupled Receptor Classification at the Subfamily Level with Probabilistic Suffix Tree

Using Support Vector Machines for Numerical Prediction

Two novel methods for multiclass ECG arrhythmias classification based on PCA, fuzzy support vector machine and unbalanced clustering

Improved Fuzzy Multicategory Support Vector Machines Classifier

Multiclass Fuzzy Clustering Support Vector Machines for Protein Local Structure Prediction