NDLI: An Ensemble Method of Adaptive Multiple Classifiers

Content Provider	IEEE Xplore Digital Library
Author	Hengzheng Kang Yan Yang Jintan Chen
Copyright Year	2010
Description	Author affiliation: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China (Hengzheng Kang; Yan Yang) \|\| School of Management, Huazhong University of Science and Technology, Wuhan, China (Jintan Chen)
Abstract	With AdaBoost being constructed, base classifiers are more and more concentrating on instances which are difficult to classify. And base classifiers are only favourite to these instances. After a base classifier is constructed, it's voting weight for final decision is determined and be the same to all test instances no matter which class a test instance belongs to. Considering these problems, the paper proposes an improved AdaBoost algorithm called AMC (An Ensemble Method of Adaptive Multiple Classifiers), which gains training set with stratified weighted sampling at first and then combines base classifiers with adaptive weight. Experiment shows the AMC has a higher accuracy to the given data set and produces higher value of recall rate and precision.
Starting Page	215
Ending Page	217
File Size	77060
Page Count	3
File Format	PDF
ISBN	9781424467914
e-ISBN	9781424467938
DOI	10.1109/ISKE.2010.5680881
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Multiple classifier Iris Accuracy Adaptive weight Heuristic algorithms Boosting Classification algorithms Diabetes Ensemble learning Stratified weighted sampling
Content Type	Text
Resource Type	Article

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