Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Feature selection for automatic classification of non-Gaussian data
IEEE Transactions on Systems, Man and Cybernetics - Special issue on 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
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data preparation for data mining
Data preparation for data mining
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Customer Retention via Data Mining
Artificial Intelligence Review - Issues on the application of data mining
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Randomized Variable Elimination
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Feature Selection via Set Cover
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
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This paper introduces concepts and algorithms for feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms by a categorizing framework based on search strategies, evaluation criteria, and data mining tasks and provides guidelines in selecting feature selection algorithms. Search strategies include complete ones, sequential ones and random ones. Evaluation criteria includes filter, wrapper and hybrid. Data mining tasks include classification and clustering. Then, a feature selecting platform is proposed as an intermediate step based on the data and requirement of the task. According to the platform and categorizing framework, some appropriate algorithms are compared. At last, an experiment based on data oilsk81, oilsk83, oilsk85 wells of Jianghan oil fields in China was operated by using one of the appropriate algorithms. This algorithm utilizes fusion of soft computing methods to distinguish the key features of reservoir oil-bearing formation and establishes a model with fusion of soft computing methods to forecast these key features. The following part is the process: Firstly, use genetic algorithm (GA) and fuzzy c-means algorithm (GA-FCM) to reduce well log features of oil-bearing formation and to obtain the key features that can describe oil-bearing formation of reservoir. Secondly, fuse genetic algorithm with BP neural network (GA-BP) to construct the fusion model that forecasts these key features. GA-BP searches the inputs and optimal number nodes of hidden layer of BP neural network through GA to choose the optimal structure of BP neural network forecasting model. Then test effectiveness of the forecasting model with recognition accuracy of testing samples. Finally, the optimal model for forecasting key features can be obtained.