New Algorithms for Generation Decision Trees—Ant-Miner and Its Modifications

In our approach we want to ensure the good performance of Ant- Miner by applying the well-known (from the ACO algorithm) two pheromone updating rules: local and global, and the main pseudo-random proportional rule, which provides appropriate mechanisms for search space: exploitation and exploration. Now we can utilize an improved expression of this classification rule discovery system as an Ant-Colony-Miner. Further modifications are connected with the simplicity of the heuristic function used in the standard Ant-Miner. We propose to employing a new heuristic function based on quantitative, not qualitative parameters used during the classification process. The main transition rule will be changed dynamically as a result of the simple frequency analysis of the number of cases from the point of view characteristic partitions. This simplified heuristic function will be compensated by the pheromone update in different degrees, which helps ants to collaborate and is a good stimulant on ants’ behavior during the rule construction. The comparative study will be conducted using 5 data sets from the UCI Machine Learning repository.