Multiple classifiers based on evolvable hardware for cancer classification with microarray data
1.College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 2.Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 3.Department of Information and Communication Engineering, Inha University, Incheon 402-751, Republic of Korea
Abstract： In order to solve the problems of low recognition rate and poor stability in a single classifier, a selective multiple classifiers ensemblebased evolvable hardware was proposed for the classification of microarray data. The proposed original training set was randomly divided into a training set and a validation set. The base evolvable hardware classifiers was trained by the partitioned training sets, and the performance of the trained base classifiers were evaluated by the validation set. The better partial base classifiers were selected to build a final ensembled classifier, and the performance of the ensembled classifier was tested with an independent test set. The experimental results show that the recognition rate of the proposed evolvable hardware for the classification of acute leukemia and colon cancer are 95.42% and 88.33%, respectively, which are higher than those of other pattern recognition methods. Compared with traditional multiple classifiers ensemblebased evolvable hardware, the proposed scheme has similar recognition rate, much lower hardware cost.
2013, Vol. 34 
