Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Learning from preferences and selected multimodal features of players
Proceedings of the 2009 international conference on Multimodal interfaces
Modeling player experience in super mario bros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Towards affective camera control in games
User Modeling and User-Adapted Interaction
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Feature selection has become a relevant and challenging problem for the area of knowledge discovery in database. An effective feature selection strategy can significantly reduce the data mining processing time, improve the predicted accuracy, and help to understand the induced models, as they tend to be smaller and make more sense to the user. In this paper, an effective research around the utilization of the Perceptron paradigm as a method for feature selection is carried out. The idea is training a Perceptron and then utilizing the interconnection weights as indicators of which attributes could be the most relevant. We assume that an interconnection weight close to zero indicates that the associated attribute to this weight can be eliminated because it does not contribute with relevant information in the construction of the class separator hyper-plane. The experiments were realized with 4 real and 11 synthetic databases. The results show that the proposed algorithm is a good trade-off among performance (generalization accuracy), efficiency (processing time) and feature reduction. Specifically, we apply the algorithm to a Mexican Electrical Billing database with satisfactory accuracy, efficiency and feature reduction results.