Improving the Performance of FLN by Using Similarity Measures and Evolutionary Algorithms

In this work, we show that the underlying inclusion measure used by fuzzy lattice neurocomputing classifiers can be extended to various similarity and distance measures often used in cluster analysis. We show that for some similarity measures, we can modify the measure to 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 provide evidence that these new techniques provide significant improvements by applying them to the Cleveland heart data.