Boolean Feature Discovery in Empirical Learning
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Data set Editing by Ordered Projection
Intelligent Data Analysis
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The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. The performance of SOAP is analyzed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [4] and ReliefF [6]. The results are generated by C4.5 before and after the application of the algorithms.