Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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In recent years there has been an explosion in the rate of acquisition of astronomical data. The analysis of astronomical data presents unprecedented opportunities and challenges for data mining in tasks, such as clustering, object discovery and classification. In this work, we address the feature selection problem in classification of photometric and spectroscopic data collected from the SDSS survey. We present a comparison of five feature selection algoritms: best first (BF), scatter search (SS), genetic algorithm (GA), best incremental ranked subset (BI) and best agglomerative ranked subset (BA). Up to now all these strategies were first applied to this paper to study relevant features in SDSS data.