Combination of PSO and AdaBoost algorithm and its application to Multi-Classification prediction of Yeast Protein Localization Sites

Author

Zhang, Yuntao ; Zhou, Changhong

Author_Institution

Inst. of Appl. Chem., China West Normal Univ., Nanchong, China

Volume

6

fYear

2010

fDate

16-18 Oct. 2010

Firstpage

2302

Lastpage

2305

Abstract

A new method PAdaBoost has been proposed to optimize the training sample of AdaBoost algorithm by using PSO, and increased the initial weights of rare class. PAdaBoost model has been used to classify the data of Yeast Protein Localization Sites, for all the data, it has improved the prediction accuracy to 66.24%, up from 59.1% and 64.89% when the best model of and AdaBoost are applied. For studying the generalization of the model, the data set is divided into a training set and a test set, and compared with the AdaBoost model, the accuracy of all data and F-value of rare class are both higher than that of PAdaBoost model. By the model validation, PAdaBoost has shown the stability generalization.

Keywords

bioinformatics; microorganisms; particle swarm optimisation; pattern classification; proteins; AdaBoost algorithm; F-value; PAdaBoost model; PSO; model validation; multiclassification prediction; particle swarm optimization; rare class; stability generalization; yeast protein localization sites; Indexes; Training; AdaBoost; PSO; multiclassification; protein localization sites;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4244-6495-1

Type

conf

DOI

10.1109/BMEI.2010.5639352

Filename

5639352