Instance-Based Learning Algorithms
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Attribute Dependencies, Understandability and Split Selection in Tree Based Models
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Error-Based Pruning of Decision Trees Grown on Very Large Data Sets Can Work!
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Restructuring decision tables for elucidation of knowledge
Data & Knowledge Engineering
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Consistency-based search in feature selection
Artificial Intelligence
A selective sampling approach to active feature selection
Artificial Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data mining in bioinformatics using Weka
Bioinformatics
Towards efficient variables ordering for Bayesian networks classifier
Data & Knowledge Engineering
A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection
IEEE Transactions on Knowledge and Data Engineering
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
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ReliefF has proved to be a successful feature selector but when handling a large dataset, it is computationally expensive. We present an optimization using Supervised Model Construction which improves starter selection. Effectiveness has been evaluated using 12 UCI datasets and a clinical diabetes database. Experiments indicate that compared with ReliefF, the proposed method improved computation efficiency whilst maintaining the classification accuracy. In the clinical dataset (20,000 records with 47 features), feature selection via Supervised Model Construction (FSSMC) reduced the processing time by 80%, compared to ReliefF, and maintained accuracy for Naive Bayes, IB1 and C4.5 classifiers.