Ensemble Pruning Using Reinforcement Learning
Lecture notes in computer sciencePublished 1 January 2006
Ioannis Partalas, Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas
Citations38
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
In this modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment, and obtains very promising results.
Abstract
Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results.
Keywords
Computer Science
Elsevier eBooksData Mining: Practical Machine Learning Tools and Techniques
25,718 Citations2011Ian H. Witten, Eibe Frank
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Choice Reviews OnlineNeural networks for pattern recognition
18,687 Citations1994
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Neural Networks for Pattern Recognition
12,198 Citations1995Chris Bishop
Machine LearningQ-learning
8,970 Citations1992Christopher J. Watkins, Peter Dayan
This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Lecture notes in computer scienceEnsemble Methods in Machine Learning
7,862 Citations2000Thomas G. Dietterich
MIT Press eBooksIntroduction to Reinforcement Learning
6,868 Citations1998Richard S. Sutton, Andrew G. Barto
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Machine LearningInstance-Based Learning Algorithms
4,117 Citations1991David W. Aha, Dennis Kibler +1 more
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Elsevier eBooksFast Effective Rule Induction
3,767 Citations1995William W. Cohen
This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Machine LearningTechnical Note: Q-Learning
3,691 Citations1992Christopher J. Watkins, Peter Dayan
A convergence theorem is presented and proves that Q -learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
arXiv (Cornell University)Estimating Continuous Distributions in Bayesian Classifiers
2,925 Citations2013George H. John, Pat Langley
Research Commons (The University of Waikato)Generating Accurate Rule Sets Without Global Optimization
1,284 Citations1998Eibe Frank, Ian H. Witten
This paper presents an algorithm for inferring rules by repeatedly generating partial decision trees, thus combining the two major paradigms for rule generation—creating rules from decision trees and the separate-and-conquer rule-learning technique.
Encyclopedia of Machine Learning and Data MiningMultiple Classifier Systems
1,041 Citations2017Claude Sammut, Geoffrey I. Webb
Ensemble selection from libraries of models
844 Citations2004Rich Caruana, Alexandru Niculescu-Mizil +2 more
A method for constructing ensembles from libraries of thousands of models using forward stepwise selection to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area is presented.
Elsevier eBooksK*: An Instance-based Learner Using an Entropic Distance Measure
752 Citations1995John G. Cleary, Leonard E. Trigg
K*, an instance-based learner that uses entropy as a distance measure, is described, and results that compare favourably with several machine learning algorithms are presented.
Lecture notes in computer scienceThe power of decision tables
707 Citations1995Ron Kohavi
Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5.
Lecture notes in computer scienceMachine Learning for Sequential Data: A Review
671 Citations2002Thomas G. Dietterich
This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems, including sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks.
Lecture notes in computer scienceMachine Learning: ECML 2004
217 Citations2004Jean‐François Boulicaut, Floriana Esposito +2 more
Algorithm Selection using Reinforcement Learning
135 Citations2000Michail G. Lagoudakis, Michael L. Littman
A kind of MDP that models the algorithm selection problem by allowing multiple state transitions is introduced, and the well known Q-learning algorithm is adapted for this case in a way that combines both Monte-Carlo and Temporal Difference methods.
Lecture notes in computer scienceEffective Voting of Heterogeneous Classifiers
48 Citations2004Grigorios Tsoumakas, Ioannis Katakis +1 more
Experimental results show that the proposed method, Effective Voting, performs comparably with the state-of-the-art method of Stacking with Multi-Response Model Trees without the additional computational cost of meta-training.
NeurocomputingOnline adaptive policies for ensemble classifiers
26 Citations2005Christos Dimitrakakis, Samy Bengio
This paper attempts to train and combine the base classifiers using an adaptive policy through a Q-learning inspired technique, and demonstrates its effectiveness for an essentially supervised task.