Using Genetic Algorithm to Optimize Weights in Data Mining Task

This paper considers an application of genetic algorithm (GA) to optimize weights in data mining task. Data mining tasks usually have datasets containing a large number of records and features that will be processed using, for example, created classification rules. As a result, by using classical method to classify a large number of records and features, a high classification error value will be obtained. To solve this problem, the genetic algorithm was applied to find for each feature the weight that would reduce classification error value. As a classical method, the k-nearest neighbour (KNN) classifier was chosen and the modified genetic algorithm was applied to optimize the weight. Based on the joint application of genetic and k-nearest neighbour algorithms, the GA/KNN hybrid algorithm was developed. As a result, the developed hybrid algorithm provides a stable classification error reducing regardless of the number of records and features, and also of the chosen number of neighbours. In the GA block the modified crossover and mutation works in each generation with identical intensity and cannot provide debasing of the individual.