Instance-Based Learning Algorithms
Machine Learning
Vector quantization and signal compression
Vector quantization and signal compression
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Practical reusable UNIX software
Practical reusable UNIX software
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
How to build a Beowulf: a guide to the implementation and application of PC clusters
How to build a Beowulf: a guide to the implementation and application of PC clusters
A Multistrategy Approach to Classifier Learning from Time Series
Machine Learning - Special issue on multistrategy learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bayesian Learning for Neural Networks
Bayesian Learning for 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
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Time Series Learning With Probabilistic Network Composites
Time Series Learning With Probabilistic Network Composites
Knowledge-guided constructive induction
Knowledge-guided constructive induction
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SoPhIA: a unified architecture for knowledge discovery
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
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We present an application of inductive concept learning and interactive visualization techniques to a large-scale commercial data mining project. This paper focuses 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. In particular, we discuss decision support issues for the application (cost prediction for automobile insurance markets in several states) and report experiments using iD2K, a Java-based visual programming system for data mining and information visualization, and several commercial and research tools. We describe exploratory clustering, descriptive statistics, and supervised decision tree learning in this application, focusing on a parallel genetic algorithm (GA) system, iJenesis, which is used to implement relevance determination (attribute subset selection). Deployed on several high-performance network-of-workstation systems (Beowulf clusters), iJenesis achieves a linear speedup, due to a high degree of task parallelism. Its test set accuracy is significantly higher than that of decision tree inducers alone and is comparable to that of the best extant search-space based wrappers.