A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm

摘要: Abstract Attribute reductions are essential pre-processing steps in such as data mining, machine learning, pattern recognition and many other fields. Moreover, test-cost-sensitive attribute often used when we have to deal with cost-sensitive data. The main result of this paper is a new meta-heuristic optimization method for finding optimal reduction that based on binary bat algorithm originally was designed model the echolocation behavior bats they search their prey. First provide 0-1 integer programming can calculate reduct but inefficient large sets. We will use it evaluate algorithms. Next, fitness function utilizes pairs inconsistent objects does not any uncertain parameter design an efficient counting provided. Then, technique uses Finally, evaluation four different metrics has been proposed algorithms only sub-optimal solutions. Several experiments were carried out broadly benchmark sets results shown superiority our algorithm, terms various metrics, computational time, classification accuracy, especially high-dimensional