Abstract: The automatic prediction and detection of breast cancer disease is an imperative, challenging problem in medical applications. In this paper, a proposed model to improve the accuracy of classification algorithms is presented. A new approach for designing effective pre-processing stage is introduced. Such approach integrates K-means clustering algorithm with fuzzy rough feature selection or correlation feature selection for data reduction. The attributes of the reduced clustered data are merged to form a new data set to be classified. Simulation results prove the enhancement of classification by using the proposed approach. Moreover, a new hybrid model for classification composed of K-means clustering algorithm, fuzzy rough feature selection and discernibility nearest neighbour is achieved. Compared to previous studies on the same data, it is proved that the presented model outperforms other classification models. The proposed model is tested on breast cancer dataset from UCI machine learning repository.

Ibrahim M. El-Hasnony, Hazem M. El-Bakry, Omar H. Al-Tarawneh and Mona Gamal. “A Proposed Hybrid Effective Technique for Enhancing Classification Accuracy”. International Journal of Advanced Computer Science and Applications (IJACSA) 8.12 (2017). http://dx.doi.org/10.14569/IJACSA.2017.081224

@article{El-Hasnony2017,
title = {A Proposed Hybrid Effective Technique for Enhancing Classification Accuracy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081224},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081224},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {12},
author = {Ibrahim M. El-Hasnony and Hazem M. El-Bakry and Omar H. Al-Tarawneh and Mona Gamal}
}