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Incremental, instance-based learning of independent and graded concept descriptions
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Instance-Based Learning Algorithms
Machine Learning
The replication problem: a constructive induction approach
EWSL-91 Proceedings of the European working session on learning on Machine learning
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Computational Intelligence
A practical approach to feature selection
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Incremental Learning from Noisy Data
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Machine learning methods for microbial source tracking
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Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Correlation based feature selection method
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Computers in Biology and Medicine
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Pattern Recognition Letters
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Sequential classifier combination for pattern recognition in wireless sensor networks
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Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
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Computer Methods and Programs in Biomedicine
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Orthogonal relief algorithm for feature selection
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Robust SVM-based biomarker selection with noisy mass spectrometric proteomic data
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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
A method for feature selection on microarray data using support vector machine
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Effective feature preprocessing for time series forecasting
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Data Mining and Knowledge Discovery
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Efficient case based feature construction
ECML'05 Proceedings of the 16th European conference on Machine Learning
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Feature selection method using preferences aggregation
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
E-Coli promoter recognition using neural networks with feature selection
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Supporting generalized cases in conversational CBR
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Personalized implicit learning in a music recommender system
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
A study of applying dimensionality reduction to restrict the size of a hypothesis space
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Temporal data mining for smart homes
Designing Smart Homes
Analysis of feature weighting methods based on feature ranking methods for classification
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Feature selection for dimensionality reduction
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An unsupervised feature selection framework based on clustering
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Large-margin feature selection for monotonic classification
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Feature selection for MAUC-oriented classification systems
Neurocomputing
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International Journal of Approximate Reasoning
Application of global optimization methods to model and feature selection
Pattern Recognition
Inferring disease-related metabolite dependencies with a bayesian optimization algorithm
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Artificial Intelligence in Medicine
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Pattern Recognition Letters
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Pattern Recognition Letters
Sensor selection to support practical use of health-monitoring smart environments
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Applied Intelligence
A novel feature subset selection algorithm based on association rule mining
Intelligent Data Analysis
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For real-world concept learning problems, feature selection is important to speed up learning and to improve concept quality. We review and analyze past approaches to feature selection and note their strengths and weaknesses. We then introduce and theoretically examine a new algorithm Rellef which selects relevant features using a statistical method. Relief does not depend on heuristics, is accurate even if features interact, and is noise-tolerant. It requires only linear time in the number of given features and the number of training instances, regardless of the target concept complexity. The algorithm also has certain limitations such as nonoptimal feature set size. Ways to overcome the limitations are suggested. We also report the test results of comparison between Relief and other feature selection algorithms. The empirical results support the theoretical analysis, suggesting a practical approach to feature selection for real-world problems.