摘要
Multiple classifier systems based on the combination of a set of different classifiers are adopted to achievehigh pattern-recognition performances. A multiple classifiers integration method based on adaptive weight adjusting ispresented in this paper. The useful neighbors are selected from training set by analyzing the pending pattern' s charac-ter, then each classifier's weight can be determined automatically by analyzing the performance of the classifier on theuseful neighborhood set. The final output of the multiple classifiers systems is the effective integration of each calssifi-er's result. The effectiveness of the method is proved by the text classification experiments of the Reuters-21578 textsets.
Multiple classifier systems based on the combination of a set of different classifiers are adopted to achieve high pattern-recognition performances. A multiple classifiers integration method based on adaptive weight adjusting is presented in this paper. The useful neighbors are selected from training set by analyzing the pending pattern's character, then each classifier's weight can be determined automatically by analyzing the performance of the classifier on the useful neighborhood set. The final output of the multiple classifiers systems is the effective integration of each calssifi-er's result. The effectiveness of the method is proved by the text classification experiments of the Reuters-21578 text sets.
出处
《计算机科学》
CSCD
北大核心
2003年第1期82-84,共3页
Computer Science
基金
北京市科委科技项目基金
基金编号为:2001-0075
