Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
Classifier systems and genetic algorithms
Artificial Intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Elements of information theory
Elements of information theory
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
A theory and methodology of inductive learning
Readings in knowledge acquisition and learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The mythical man-month (anniversary ed.)
The mythical man-month (anniversary ed.)
Machine Learning
Elements of artificial neural networks
Elements of artificial neural networks
Communications of the ACM
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Bayesian network models for generation of crisis management training scenarios
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Radial basis function networks
The handbook of brain theory and neural networks
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Multistrategy Approach to Classifier Learning from Time Series
Machine Learning - Special issue on multistrategy learning
Introductory Combinatorics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Change of Representation and Inductive Bias
Change of Representation and Inductive Bias
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Introduction to Algorithms
High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application
Data Mining and Knowledge Discovery
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Knowledge-guided constructive induction
Knowledge-guided constructive induction
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Time series learning with probabilistic network composites
Time series learning with probabilistic network composites
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A new metric-based approach to model selection
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Information Sciences: an International Journal - Special issue: Soft computing data mining
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In this chapter, I discuss the problem of feature subset selection for supervised inductive learning approaches to knowledge discovery in databases (KDD), and examine this and related problems in the context of controlling inductive bias. I survey several combinatorial search and optimization approaches to this problem, focusing on data-driven, validation-based techniques. In particular, I present a wrapper approach that uses genetic algorithms for the search component, using a validation criterion based upon model accuracy and problem complexity, as the fitness measure. Next, I focus on design and configuration of high-level optimization systems (wrappers) for relevance determination and constructive induction, and on integrating these wrappers with elicited knowledge on attribute relevance and synthesis. I then discuss the relationship between this model selection criterion and those from the minimum description length (MDL) family of learning criteria. I then present results on several synthetic problems on task-decomposable machine learning and on two large-scale commercial data-mining and decision-support projects: crop condition monitoring, and loss prediction for insurance pricing. Finally, I report experiments using the Machine Learning in Java (MLJ) and Data to Knowledge (D2K) Java-based visual programming systems for data mining and information visualization, and several commercial and research tools. Test set accuracy using a genetic wrapper is significantly higher than that of decision tree inducers alone and is comparable to that of the best extant search-space based wrappers.