An Ensemble Pruning Primer
نویسندگان
چکیده
Ensemble pruning deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. The last 12 years a large number of ensemble pruning methods have been proposed. This work proposes a taxonomy for their organization and reviews important representative methods of each category. It abstracts their key components and discusses their main advantages and disadvantages. We hope that this work will serve as a good starting point and reference for researchers working on the development of new ensemble pruning methods.
منابع مشابه
Multilayer Ensemble Pruning via Novel Multi-sub-swarm Particle Swarm Optimization
Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers were developed during the past decade. When these diverse classifiers are generated, it is important to select the proper base classifier to j...
متن کاملAn Empirical Comparison of Pruning Methods for Ensemble Classifiers
Many researchers have shown that ensemble methods such as Boosting and Bagging improve the accuracy of classification. Boosting and Bagging perform well with unstable learning algorithms such as neural networks or decision trees. Pruning decision tree classifiers is intended to make trees simpler and more comprehensible and avoid over-fitting. However it is known that pruning individual classif...
متن کاملPareto Ensemble Pruning
Ensemble learning is among the state-of-the-art learning techniques, which trains and combines many base learners. Ensemble pruning removes some of the base learners of an ensemble, and has been shown to be able to further improve the generalization performance. However, the two goals of ensemble pruning, i.e., maximizing the generalization performance and minimizing the number of base learners...
متن کاملPersonalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
Ensemble learning has been widely employed by mobile applications, ranging from environmental sensing to activity recognitions. One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning. During crowdsourcing, the centralized aggregator releases ensemble learning models to a large number of mo...
متن کاملA competitive ensemble pruning approach based on cross-validation technique
Ensemble pruning is crucial for the considerations of both efficiency and predictive accuracy of an ensemble system. This paper proposes a new Competitive measure for Ensemble Pruning based on Cross-Validation technique (CEPCV). Firstly, the data to be learnt by neural computing models are mostly drifting with time and environment, while the proposed CEPCV method can realize on-line ensemble pr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009