Affiliations: [a] Department of Computer Science, Utkal University, Vani Vihar, Odisha, India | [b] Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA | [c] IT4Innovations, Center of Excellence VSB, Technical University of Ostrava, Ostrava, Poruba, Czech Republic | [d] School of Computing Science and Engineering, VIT University, Vellore, Tamilnadu, India
Correspondence:
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Corresponding author: Mrutyunjaya Panda, Department of Computer Science, Utkal University, Vani Vihar, Bhubaneswar-4, Odisha, India. E-mail:[email protected]
Abstract: This paper aims at providing the concept of information granulation in
Granular computing based pattern classification that is used to deal with
incomplete, unreliable, uncertain knowledge from the view of a dataset. Data
Discretization provides us the granules which further can be used to
classify the instances. We use Equal width and Equal frequency
Discretization as unsupervised ones; Fayyad-Irani's Minimum description
length and Kononenko's supervised discretization approaches along with Fuzzy
logic, neural network, Support vector machine and their hybrids to develop
an efficient granular information processing paradigm. The experimental
results show the effectiveness of our approach. We use benchmark datasets in
UCI Machine Learning Repository in order to verify the performance of
granular computing based approach in comparison with other existing
approaches. Finally, we perform statistical significance test for confirming
validity of the results obtained.
