Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Conventional cut selection in Boolean reasoning (BR) based discretization often produces under-optimistic prime cuts. This is due to the linearity of traditional heuristics in tackling high-dimensional space problem. We proposed a flexible yet compact and holistic solution by incorporating Particle Swarm Optimization (PSO) into the existing framework. The first challenge is to downsize the search space such that the probability of finding the global optimum is increased. The second task is to reconstruct the present fitness function so as to improve the classification performance of the induction algorithm, which in this case, C4.5. By injecting a filtration phase prior to the cut selection and introducing a tertiary term to the fitness function, the proposed extended BR with PSO (EBRPSO) discretizer is developed. Based on the evaluation using four real-world datasets (i.e.: Heart, Breast, Iris and Wine), it is proven that EBRPSO outperforms the existing discretizers in terms of classification accuracy as well as reduction of the decision rules.
boolean reasoning particle swarm optimization discretization rough sets theory