Bagging and Boosting with Dynamic Integration of Classifiers
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Local Feature Selection with Dynamic Integration of Classifiers
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Decision Committee Learning with Dynamic Integration of Classifiers
ADBIS-DASFAA '00 Proceedings of the East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications: Current Issues in Databases and Information Systems
Advanced Local Feature Selection in Medical Diagnostics
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine
Computer Methods and Programs in Biomedicine
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
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In many application areas, as in medicine, knowledge is discovered from large databases using data mining methods. It is evident that quite often even with static data mining methods the accuracy of the results of data mining is better if fewer features are selected or if the classification task is divided into sub tasks guided by the heterogeneity of the feature space. On the other hand, one of the most important directions in the improvement of data mining and knowledge discovery is to integrate multiple classification techniques included in an ensemble of classifiers. We present two variations of an advanced dynamic integration technique, which first build an ensemble of classifiers containing base classifiers based on the subsets of the original feature set, then evaluate the competence areas of the base classifiers inside the application domain, and finally select a classifier to produce the final classification result dynamically. The technique is evaluated on two data sets taken from the UCI machine learning repository and a comparison of the results show that our dynamic integration technique with classifiers based on reduced feature sets is able to produce more accurate results than C4.5 with the whole set of features on those data sets and that our method outperforms the cross-validation majority technique and is comparable with weighted voting on some data sets.