Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TLDR
The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.About:
This article is published in Artificial Intelligence.The article was published on 1997-12-01 and is currently open access. It has received 8610 citations till now. The article focuses on the topics: Feature selection & Minimum redundancy feature selection.read more
Citations
More filters
Proceedings ArticleDOI
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
TL;DR: A novel filter bank common spatial pattern (FBCSP) is proposed to perform autonomous selection of key temporal-spatial discriminative EEG characteristics and shows that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
Proceedings ArticleDOI
Unsupervised feature selection for multi-cluster data
TL;DR: Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, a new approach is proposed, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection, which select those features such that the multi-cluster structure of the data can be best preserved.
Proceedings ArticleDOI
Adversarial classification
TL;DR: This paper views classification as a game between the classifier and the adversary, and produces a classifier that is optimal given the adversary's optimal strategy, and experiments show that this approach can greatly outperform a classifiers learned in the standard way.
Journal ArticleDOI
Feature Selection for Unsupervised Learning
Jennifer G. Dy,Carla E. Brodley +1 more
TL;DR: This paper explores the feature selection problem and issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood.
Journal ArticleDOI
Data preprocessing techniques for classification without discrimination
Faisal Kamiran,Toon Calders +1 more
TL;DR: This paper surveys and extends existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances and presents the results of experiments on real-life data.
References
More filters
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Classification and Regression Trees.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book
Applied Regression Analysis
Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.