[1]CHEN Xiao-feng,WANG Shi-tong,CAO Su-qun.Gene function analysis of semisupervised multilabel learning[J].CAAI Transactions on Intelligent Systems,2008,3(1):83-90.
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Gene function analysis of semisupervised multilabel learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 3 Number of periods: 2008 1 Page number: 83-90 Column: 学术论文—机器学习 Public date: 2008-02-25
Title:
Gene function analysis of semisupervised multilabel learning
Author(s):
CHEN Xiao-feng1;  WANG Shi-tong1;  CAO Su-qun1; 2
1.School of Information Technology, Jiangnan University, Wuxi 214122 , Ch ina;
?2.Department of Mechanical Engineering, Huaiyin Institute of Technology, H uai’an 223001,China
Keywords:
semisupervised;  multilabel;  selftraining;  support vector machine
CLC:
TP181
DOI:
-
Abstract:
Conventional machine learning is used only for single label learning, implying that every sample has only one label. However, in bioinformatics, a gen e has more than one function, so it needs more than one label. Therefore, multi  label learning is more effective for identifying gene groups than conventional l earning approach. Current research mainly focuses on supervised multilabel lea r ning. The problem of effective semisupervised multilabel learning strategies f or labeled examples and unlabeled examples of gene expression datasets still rem ains unsolved. In this paper, a semisupervised multilabel learning algorithm , named SML_SVM, is presented as an effective multilabel learner for analysis of gene expressions with at least one function. First, the proposed SML_SVM algorit hm transforms the semisupervised multilabel learning into corresp ond ing semisupervised singlelabel learning by the PT4 method, then it labels un la beled examples using the maximum a posteriori (MAP) principle in combination wit h the Knearest neighbor method, and finally, it solves the corresponding singl e label learning problem using SVM. The distinctive characteristic of the propos e d algorithm is its efficient integration of SVMbased singlelabel learning wi th MAP and Knearest neighbor methods. Experimental results with a real Yeast gen e expression dataset and a Genbase protein dataset show that the proposed SML_S VM algorithm outperforms the PT4based MLSVM method and selftraining MLSVM.
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