Hybrid approach for diagnosing thyroid, hepatitis, and breast cancer based on correlation based feature selection and Naïve bayes

Authors:
Mohammad Ashraf;Girija Chetty;Dat Tran;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

Citing 9
Cited 0

Estimating attributes: analysis and extensions of RELIEF

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Chi2: Feature Selection and Discretization of Numeric Attributes

TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection

The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining

IEEE Transactions on Knowledge and Data Engineering
Naive Bayes for optimal ranking

Journal of Experimental & Theoretical Artificial Intelligence
Data Mining with Decision Trees: Theroy and Applications

Data Mining with Decision Trees: Theroy and Applications
Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I

ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature selection techniques have become an obvious need for researchers in computer science and many other fields of science. Whether the target research is in medicine, agriculture, business, or industry; the necessity for analysing large amount of data is needed. In Addition to that, finding the most excellent feature selection technique that best satisfies a certain learning algorithm could bring the benefit for researchers. Therefore, we proposed a new method for diagnosing some diseases based on a combination of learning algorithm tools and feature selection techniques. The idea is to obtain a hybrid approach that combines the best performing learning algorithms and the best performing feature selection techniques in regards to three well-known datasets. Experimental result shows that co-ordination between correlation based feature selection method along with Naive Bayse learning algorithm can produce promising results.