A Novel Fitness Computation Framework for Nature Inspired Classification Algorithms

Nature inspired algorithms have become popular for discovering classification rules due to their ability to effectively handle large and complex search spaces. However, nature inspired algorithms have to compute the fitness of individuals (candidate classification rules) over successive generations repeatedly. Each fitness computation requires scanning the training data. Since the database scan is computationally expensive operation, the execution time for nature inspired algorithms for discovering classification rules grows unreasonably faster for bulky datasets. This paper proposes a novel fitness computation framework for nature inspire algorithms by using a list structure. The indices of instances, covered by every possible attribute-value pair with respect to each class in the training data, are stored in the suggested list structure. The list is prepared only once in advance and stores all information to compute the fitness of any rule that may come up in the life time of the nature inspired algorithm. The existence of the pre-maintained list eliminates the need of scanning training data again and again for fitness computation. We have conducted experiments on 12 datasets from UCI machine learning repository. The results show that the suggested fitness computational framework brings significant speed gain without compromising predictive accuracy. Although, a Genetic Algorithm is used for classification rule discovery as the nature inspired algorithm in this paper, the fitness computation framework is general and can be used with any other nature inspired algorithm.