標題: A Hybrid Memetic Algorithm for Simultaneously Selecting Features and Instances in Big Industrial IoT Data for Predictive Maintenance
作者: Liang, Yu-Lin
Kuo, Chih-Chi
Lin, Chun-Cheng
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: instance selection;feature selection;noisy data;evolutionary computation;big data;machine learning
公開日期: 1-Jan-2019
摘要: In Industry 4.0, various types of IoT sensors which are installed on machines to collect data for predictive maintenance. As the collected data increases, there are more missing values and noisy data. Related studies have already proposed various methods to solve the problems in big data. Among them, most studies focused on either feature selection or instance selection for data preprocessing before training forecast models. Metaheuristic algorithm is one of the mainstream methods in data preprocessing. However, most of these studies rarely considered feature and instance selection simultaneously. In addition, they seldom focused on noisy data. Therefore, this work combines the UCI datasets with noisy data to simulate the real situation. Memetic algorithm (MA) has excellent performance in machine learning of data selection, and variable neighborhood search (VNS) was also proved to be widely applied to the systematic change of local search algorithms. This work proposes a hybrid MA and VNS to find a new subset that maximizes the accuracy of the classifier while preserving the minimum amount of data by feature and instance selection simultaneously. Experimental results show that the proposed method can efficiently reduce the amount of data and the ratio of noisy data. By comparison with other metaheuristic algorithms, the proposed method has good performance by an excellent balance between exploration and exploitation.
URI: http://hdl.handle.net/11536/154279
ISBN: 978-1-7281-2927-3
ISSN: 1935-4576
期刊: 2019 IEEE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN)
起始頁: 1266
結束頁: 1270
Appears in Collections:Conferences Paper