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Classification of breast cancer dataset by different classification algorithms

Publication Type : Conference Paper

Publisher : 2017 4th International Conference on Advanced Computing and Communication Systems

Source : 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030233682&doi=10.1109%2fICACCS.2017.8014573&partnerID=40&md5=a06ce08219adc93fa6279ec8daf114a9

ISBN : 9781509045594

Keywords : Baggings, C4.5 algorithm, Classification (of information), Data mining, Decision tables, Decision theory, Decision trees, Hybrid classifier, K-nearest neighbors, Multi-class classifier, Nearest neighbor search, Optimization, Reduced-error pruning, Sequential minimal optimization, Support vector machines, Trees (mathematics)

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2017

Abstract : The experimental results show that the classification result with the decision trees algorithm come up over the other classifier. The decision tree algorithm creates a predictive model that predicts the state of the affected tissue by learning simple decision rules inferred while learning.

Cite this Research Publication : S. Sathya, Joshi, S., and Dr. Padmavathi S., “Classification of breast cancer dataset by different classification algorithms”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, 2017.

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