Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Abstract

Abstract-Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) metaheuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while µAnt-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman's statistical test.

Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

“…The study proposed a random selection method to determine the values of the α and β parameters [48]. Other studies have determined the class consequent in advance and then constructed the rule for this class, allocating different types of pheromone for each class [49]. Different quality functions, and each ant in the colony must select the quality functions before the rule construction process, as proposed by Rajpiplawala & Singh (2014) [50].…”

Rule pruning techniques in the ant-miner classification algorithm and its variants: A review

“…The class value of the dataset can potentially change during this procedure, because the majority of classes in the instances covered by the pruned rule might be changed compared with those covered by original rules [9,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Furthermore, the algorithms proposed by [33][34][35][36][37][38] still use the same traditional procedure to prune the rule, but they introduce a new fitness function to test quality. In addition, a dubbed threshold-aware pruning mechanism and new fitness function are used in algorithms [39,40] sensible to the order of terms that contain consistent continuous values.…”