A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases

“…And many systems have been developed [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In this study, called as Discrete Wavelet Entropy Energy and Jensen Shannon, Hellinger, Triangle Measure Classifier (DWEE-JHT) was introduced new method for diagnosis of breast cells from microscopic images independent of rotation and scaling.…”

Section: Resultsmentioning

confidence: 99%

A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT

Korkmaz

Poyraz

2014

J Med Syst

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In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE--JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis.

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“…Bagging (bootstrap aggregating) is an ensemble method that creates different training sets by sampling with replacement from the original dataset on training individual classifiers [50,52]. It provides diversity in subsets that might reduce the variance of datasets and improve results of classification algorithms [53].…”

Section: Homogeneous Classifiers Use Different Data Sets With the Sammentioning

confidence: 99%

Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Panthong

Srivihok

2019

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Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

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A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases

Cited by 29 publications

References 28 publications

A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation

A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation

A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT

Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

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