A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data. (August 2016)

• Record Type: Journal Article
• Authors: Silva Filho, Telmo M.; Souza, Renata M.C.R.; Prudêncio, Ricardo B.C.
• Is Part Of: Neural networks. Volume 80(2016)
• Page Start: 19
• Page End: 33
• Publication Date: 2016-08
• Subjects: Swarm optimization -- Weighted distance -- Prototype learning -- Feature selection -- Symbolic data analysis -- Interval data; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.04.006
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets.