THEORETICAL FOUNDATIONS AND EXPERIMENTAL RESULTS FOR A HIERARCHICAL CLASSIFIER WITH OVERLAPPING CLUSTERS. (11th September 2012)

• Record Type: Journal Article
• Authors: Podolak, Igor T.; Roman, Adam
• Is Part Of: Computational intelligence. Volume 29:Number 2(2013:May)
• Page Start: 357
• Page End: 388
• Publication Date: 2012-09-11
• Subjects: Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/j.1467-8640.2012.00469.x
• Languages: English
• ISSNs: 0824-7935

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>This paper proposes a classification framework based on simple classifiers organized in a tree‐like structure. It is observed that simple classifiers, even though they have high error rate, find similarities among classes in the problem domain. The authors propose to trade on this property by recognizing classes that are mistaken and constructing overlapping subproblems. The subproblems are then solved by other classifiers, which can be very simple, giving as a result a hierarchical classifier (HC). It is shown that HC, together with the proposed training algorithm and evaluation methods, performs well as a classification framework. It is also proven that such constructs give better accuracy than the root classifier it is built upon.</p> </abstract>