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Journal : International Journal of Electrical and Computer Engineering

Feature Selection Mammogram based on Breast Cancer Mining Shofwatul Uyun; Lina Choridah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.742 KB) | DOI: 10.11591/ijece.v8i1.pp60-69

Abstract

The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.
Feature selection for multiple water quality status: Integrated bootstrapping and SMOTE approach in imbalance classes Shofwatul Uyun; Eka Sulistyowati
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (551.021 KB) | DOI: 10.11591/ijece.v10i4.pp4331-4339

Abstract

STORET is one method to determine the river water quality into four classes (very good , good, medium and bad) based on the data of water for each attribute or feature. The success of the formation of pattern recognition model much depends on the quality of data. There are two issues as the concern of this research as follows: the data having disproportionate amount among the classes (imbalance class) and the finding of noise on its attribute. Therefore, this research integrates the SMOTE Technique and bootstrapping to handle the problem of imbalance class. While an experiment is conducted to eliminate the noise on the attribute by using some feature selection algorithms with filter approach (information gain, rule, derivation, correlation and chi square). This research has some stages as follows: data understanding, pre-processing, imbalance class, feature selection, classification and performance evaluation. Based on the result of testing using 10-fold cross validation, it shows that the use of the SMOTE-bootstrapping technique is able to increase the accurate value from 83.3% to be 98.8%. While the process of noise elimination on the data attribute is also able to increase the accuracy to be 99.5% (the use of feature subset produced by the information gain algorithm and the decision tree classification algorithm).