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      Feature interval learning algorithms for classification

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      Author
      Dayanik, A.
      Date
      2010
      Source Title
      Knowledge-Based Systems
      Print ISSN
      0950-7051
      Publisher
      Elsevier BV
      Volume
      23
      Issue
      5
      Pages
      402 - 417
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      This paper presents Feature Interval Learning algorithms (FIL) which represent multi-concept descriptions in the form of disjoint feature intervals. The FIL algorithms are batch supervised inductive learning algorithms and use feature projections of the training instances to represent induced classification knowledge. The concept description is learned separately for each feature and is in the form of a set of disjoint intervals. The class of an unseen instance is determined by the weighted-majority voting of the feature predictions. The basic FIL algorithm is enhanced with adaptive interval and feature weight schemes in order to handle noisy and irrelevant features. The algorithms are empirically evaluated on twelve data sets from the UCI repository and are compared with k-NN, k-NNFP, and NBC classification algorithms. The experiments demonstrate that the FIL algorithms are robust to irrelevant features and missing feature values, achieve accuracy comparable to the best of the existing algorithms with significantly less average running times. © 2010 Elsevier B.V. All rights reserved.
      Keywords
      Adaptive feature weights
      Classification learning
      Feature partitioning
      Inductive learning
      Permalink
      http://hdl.handle.net/11693/22278
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.knosys.2010.02.002
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