Building expert systems
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Evaluation of the branch and bound algorithm for feature selection
Pattern Recognition Letters
C4.5: programs for machine learning
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The engineering of knowledge-based systems: theory and practice
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Fundamentals of algorithmics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Applied Intelligence
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Decision-making processes in pattern recognition (ACM monograph series)
Decision-making processes in pattern recognition (ACM monograph series)
Feature selection in scientific applications
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data pre-processing: a new algorithm for feature selection and data discretization
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
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IEEE Transactions on Neural Networks
A performance study of gaussian kernel classifiers for data mining applications
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
Using data mining to enable integration of wind resources on the power grid
Statistical Analysis and Data Mining
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Knowledge acquisition is the process of collecting domain knowledge, documenting the knowledge, and transforming it into a computerized representation. Due to the difficulties involved in eliciting knowledge from human experts, knowledge acquisition was identified as a bottleneck in the development of knowledge-based system. Over the past decades, a number of automatic knowledge acquisition techniques have been developed. However, the performance of these techniques suffers from the so called curse of dimensionality, i.e., difficulties arise when many irrelevant (or redundant) parameters exist. This paper presents a heuristic approach based on statistics and greedy search for dimensionality reduction to facilitate automatic knowledge acquisition. The approach deals with classification problems. Specifically, Chi-square statistics are used to rank the importance of individual parameters. Then, a backward search procedure is employed to eliminate parameters (less important parameters first) that do not contribute to class separability. The algorithm is very efficient and was found to be effective when applied to a variety of problems with different characteristics.