Abstract
Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms: However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.
Keywords
Mutual informationFeature selectionPointwise mutual informationComputer scienceArtificial intelligencePattern recognition (psychology)Feature (linguistics)Interaction informationRelevance (law)Selection (genetic algorithm)Data miningConditional mutual informationInformation gainWindow (computing)Machine learningMathematicsStatistics
Affiliated Institutions
Related Publications
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion base...
Bayesian clustering methods for morphological analysis of MR images
Determining the relationship between structure (i.e. morphology) and function is a fundamental problem in brain research. In this paper we present a new framework based on Bayes...
Application of Model-Selection Criteria to Some Problems in Multivariate Analysis
A review of model-selection criteria is presented, with a view toward showing their similarities. It is suggested that some problems treated by sequences of hypothesis tests may...
Backpropagation training for multilayer conditional random field based phone recognition
Conditional random fields (CRFs) have recently found increased popularity in automatic speech recognition (ASR) applications. CRFs have previously been shown to be effective com...
Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier
A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naïve...
Publication Info
- Year
- 2002
- Type
- article
- Volume
- 24
- Issue
- 12
- Pages
- 1667-1671
- Citations
- 640
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
640
OpenAlex
Cite This
Nojun Kwak,
Chong‐Ho Choi
(2002).
Input feature selection by mutual information based on Parzen window.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 24
(12)
, 1667-1671.
https://doi.org/10.1109/tpami.2002.1114861
Identifiers
- DOI
- 10.1109/tpami.2002.1114861