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Approaching Rules Induction: CN2 Algorithm in Categorizing of Biodiversity

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Approaching Rules Induction: CN2 Algorithm in Categorizing of Biodiversity


Su Myo Swe | Khin Myo Sett



Su Myo Swe | Khin Myo Sett "Approaching Rules Induction: CN2 Algorithm in Categorizing of Biodiversity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4, June 2019, pp.1581-1584, URL: https://www.ijtsrd.com/papers/ijtsrd25153.pdf

Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" from data, without being explicitly programmed. Machine learning applications are classification, regression, clustering, density estimation and dimensionality reduction. The CN2 algorithm is a classification technique designed for the efficient induction of simple, comprehensible rules of form “if cond then predict class”, even in domains where noise may be present. Biodiversity means biological diversity, the variety of life found in a place on Earth or, often, the total variety of life on Earth. This research used butterflies as biological dataset for categorizing biodiversity and passed it to CN2 Rule Induction. In this research, “The Fauna of British India, Ceylon and Burma. Butterflies. Vol. I and Vol. II” written by C.T Bingham are used as the required knowledge for resource and categorizing biodiversity of butterfly families by rules induction with CN2 algorithm system has developed. In this system, MS Visual Studio as a programming tool and MS SQL Server as for database development are used.

Machine Learning, Rule Induction, CN2 Algorithm, Biodiversity


IJTSRD25153
Volume-3 | Issue-4, June 2019
1581-1584
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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