A New Method to Measure the Similarity between Features in Machine Learning using the Triangular Fuzzy Number
Hassan Nosrati Nahook1, Mahdi Eftekhari2
1Hassan Nosrati Nahook, Student of MSc Computers – AI, Computer Engineering Department, Science and Research Branch, Islamic Azad University, Kerman, Iran .
2Mahdi Eftekhari, Assistant Professor, Computer Engineering Department, Science and Research Branch, Islamic Azad University, Kerman, Iran.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 511-515 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1246112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we present a new method to measure the similarity between features using fuzzy numbers. The proposed method uses the concept of geometry to calculate the degree of similarity between triangular fuzzy numbers defined on the features. We also prove some properties of the proposed similarity measure and use different data sets to compare the proposed method with existing methods. In the feature selection methods, the proposed similarity measure compared with other fuzzy similarity measures can be more efficient.
Keywords: Similarity Measure, Symmetrical or Asymmetrical Triangular Fuzzy Number, Features.