Novel data condensing method using a prototype׳s front propagation algorithm

• Published in: Engineering applications of artificial intelligence Vol. 39; pp. 181 - 197
• Main Authors: Pérez-Benítez, J.A., Pérez-Benítez, J.L., Espina-Hernández, J.H.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2015
• Subjects: Data classification; Data prototypes; KNN; Machine learning; Data classification; KNN; Data prototypes; Machine learning
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2014.11.011

This work proposes a method for data condensing. The method is based on the selection of a generator of data prototypes. An algorithm for the front propagation of the prototypes׳ boundaries is performed in order to obtain the class boundaries given by a set of support vectors. The proposed method just has one tuning parameter and presents high classification rates even for complex topological and non-concave classes and low tendency to over-fitting. The most important advantage of the proposed method is its higher condensing rate without a significant detrimental effect on the classification rate. The algorithm is intended to be applied for condensing data in low memory devices and transmission of high-volume of data where data condensing could be crucial.