Beta Scale Invariant Map. (March 2017)

• Record Type: Journal Article
• Authors: Quintián, Héctor; Corchado, Emilio
• Is Part Of: Engineering applications of artificial intelligence. Volume 59(2016:Nov.)
• Page Start: 218
• Page End: 235
• Publication Date: 2017-03
• Subjects: Topology preserving maps -- Quality measures -- SIM -- MLHL-SIM -- ViSOM -- GNG -- Beta distribution -- Imbalanced datasets; 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.2017.01.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this study we present a novel version of the Scale Invariant Map (SIM) called Beta-SIM, developed to facilitate the clustering and visualization of the internal structure of complex datasets effectively and efficiently. It is based on the application of a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution, when applied to the Scale Invariant Map. The Beta-SIM behavior is thoroughly analyzed and successfully demonstrated over 2 artificial and 16 real datasets, comparing its results, in terms of three performance quality measures with other well-known topology preserving models such as Self Organizing Maps (SOM), Scale Invariant Map (SIM), Maximum Likelihood Hebbian Learning-SIM (MLHL-SIM), Visualization Induced SOM (ViSOM), and Growing Neural Gas (GNG). Promising results were found for Beta-SIM, particularly when dealing with highly complex datasets.