Classification of breast cancer dataset by different classification algorithms

Publication Type:

Conference Paper

Authors:

Sathya, S.; Joshi, S.; Padmavathi, S.

Source:

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

ISBN:

9781509045594

URL:

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

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)

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. © 2017 IEEE.

Notes:

cited By 0; Conference of 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017 ; Conference Date: 6 January 2017 Through 7 January 2017; Conference Code:130103

Cite this Research Publication

S. Sathya, Joshi, S., and 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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