On learning to predict web traffic
Decision Support Systems - Special issue: Web data mining
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Feature Selection in Genetic Fuzzy Discretization for the Pattern Classification Problems
IEICE - Transactions on Information and Systems
Proceedings of the 11th International Conference on Electronic Commerce
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Applying cost sensitive feature selection in an electric database
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Improved binary particle swarm optimization using catfish effect for feature selection
Expert Systems with Applications: An International Journal
Power system database feature selection using a relaxed perceptron paradigm
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Using ensemble feature selection approach in selecting subset with relevant features
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An optimization approach for feature selection in an electric billing database
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Feature selection in an electric billing database considering attribute inter-dependencies
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Hi-index | 0.00 |
Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time has spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. The data used as input to any of these learning systems are the primary source of knowledge in terms of what is learned by these systems. There have been relatively few studies on preprocessing data used as input in these data mining systems. In this study, we evaluate several feature selection methods as to their effectiveness in preprocessing input data. We use real-world financial credit-risk data in evaluating these systems.