Classification is a data mining technique which is utilized to predict the future by using available data and aims to discover hidden relationships between variables and classes. Since the cost component is crucial in most real life classification problems and most traditional classification methods work for the purpose of correct classification, developing cost-sensitive classifiers which minimize the total misclassification cost remains a subject of much interest. The purpose of this study is to present an effective solution method that configurates and evaluates learning systems from previous experiences, thus aiming to obtain decisions and predictions. Since most real life problems are cost-sensitive and developing effective direct methods for cost-sensitive multi-class classification is still an attractive area, a cost-sensitive classification method, the BEE-Miner algorithm, is proposed by utilizing the recently developed Bees Algorithm (BA). The main advantages of BEE-Miner are its capability to handle both binary and multi-class problems and to incorporate misclassification cost into the algorithm via generating neighbor solutions and evaluating the quality of the solutions. An extensive computational study is also performed on cost-insensitive and cost-sensitive versions of the proposed BEE-Miner algorithm and effective results on different types of problems are obtained with high test accuracy and low misclassification cost. (C) 2015 Elsevier B.V. All rights reserved.