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
Fundamentals of algorithmics
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on 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
Neural-network feature selector
IEEE Transactions on Neural Networks
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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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. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going to use, that normally will be compensated, in greater or smaller extent, by the attribute reduction in the classification model. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(mn 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; with no need for transformation. The performance of SOAP is analysed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [6] and ReliefF [11]. The results are generated by C4.5 and 1NN before and after the application of the algorithms.