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 ad...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.展开更多
文摘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.
