Novel swarm optimization for mining classification rules on thyroid gland data
This work uses a novel rule-based classifier design method, constructed by using improved simplified swarm optimization (SSO), to mine a thyroid gland dataset from UCI databases. An elite concept is added to the proposed method to improve solution quality, close interval encoding (CIE) is added to efficiently represent the rule structure, and the orthogonal array test (OAT) is added to powerfully prune rules to avoid over-fitting the training dataset. To evaluate the classification performance of the proposed improved SSO, computer simulations are performed on well-known thyroid gland data. Computational results compare favorably with those obtained using existing algorithms such as conventional classifiers, including Bayes classifier, k-NN, k-Means, and 2D-SOM, and soft computing based methods such as the simple SSO, immune-estimation of distribution algorithms (IEDA), and genetic algorithm (GA).
classification rules thyroid gland data simplified swarm optimization (sso) orthogonal array test (oat) data mining