Journal of Engineering Science and Technology Vol. 12, No. 6 (2017) 1446 - 1459 © School of Engineering, Taylor’s University 1446 AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION CHANDRASEKAR RAVI, NEELU KHARE* School of Information Technology and Engineering (SITE), VIT University, Vellore Campus, Vellore-632014, Tamil Nadu, India *Corresponding Author: neelu.khare@vit.ac.in Abstract Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS) were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS). Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSO- AdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc. Keywords: Association mining, Class-based closed frequent itemset, Tree structure, Ensemble classifier, Optimization.