Multitarget prediction—A new approach using sphere complex fuzzy sets. (March 2019)

• Record Type: Journal Article
• Authors: Tu, Chia-Hao; Li, Chunshien
• Is Part Of: Engineering applications of artificial intelligence. Volume 79(2019)
• Page Start: 45
• Page End: 57
• Publication Date: 2019-03
• Subjects: Multitarget prediction -- Complex fuzzy set -- Sphere complex fuzzy set -- Cross-target feature selection -- Influence information; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.11.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper proposes a multitarget prediction approach using a new type of sphere complex fuzzy sets. The proposed model can adjust the number of outputs depending on the application. Moreover, a corresponding feature selection algorithm based on influence information and selection gain is proposed to solve the problem of cross-target feature selection in multitarget prediction. The proposed approach is tested using three experiments. The experimental results reveal that the model can predict at least four targets simultaneously and that the proposed feature selection algorithm can, in most cases, quickly determine a favorable feature combination. Highlights: This paper proposes novel sphere complex fuzzy sets (SCFSs), in contrast to conventional fuzzy sets, to improve modeling flexibility on the number of outputs, without significantly increasing parameters. A modified PSO–RLSE hybrid learning algorithm is used to optimize the parameters of the multitarget prediction model based on the strategy of divide-and-conquer. A new feature selection algorithm based on influence information and selection gain is presented for the cross-target feature selection problem derived from multi-target prediction.