Advertisement

Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measures

  • Al Cripps
  • Nghiep Nguyen
Part of the Studies in Computational Intelligence book series (SCI, volume 67)

Summary. In this work, we show that the underlying inclusion measure used by fuzzy lattice reasoning (FLR) classiffiers can be extended to various similarity and distance measures often used in cluster analysis. We show that for the cosine similarity measures, we can weigh the contribution of each attribute found in the data set. Furthermore, we show that evolutionary algorithms such as genetic algorithms, tabu search, particle swarm optimization, and differential evolution can be used to weigh the importance of each attribute and that this weighting can provide additional improvements over simply using the similarity measure. We present experimental evidence that the proposed techniques imply significant improvements.

Keywords

Particle Swarm Optimization Similarity Measure Evolutionary Algorithm Tabu Search Candidate Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Al Cripps
    • 1
  • Nghiep Nguyen
    • 1
  1. 1.Dept Computer ScienceMiddle Tennessee State UniversityMurfreesboroUSA

Personalised recommendations