Ensemble Feature Ranking Applied to Medical Data

Santos, Vítor; Datia, Nuno; Pato, Matilde

doi:10.1016/j.protcy.2014.10.232

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…Parkinsons 16 10,21,22,17,20,18,14,9,11,12,13,5,7,4,15,8 The feature reduction achieved for CKD, Breast Cancer Wisconsin Dataset, and Parkinsons Dataset are 24%, 21.8%, and 30.4% respectively. The feature subset derived after the proposed feature selection algorithm consists of the most discriminatory features that enhances the accuracy of prediction of chronic diseases without any loss in the original information.The reduced feature subset is evaluated against three classification algorithms SVM, CNN, and Gradient Boosting.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The performance of the proposed method is compared with CfsSubsetEval, ConsistencySubsetEval, SF with Entropy and the results reveal that this method better classification accuracy for the reduced feature subset. V´ıtor Santos et al [9] have applied FS techniques to large datasets using Feature Ranking (FR) algorithms. The feature subset is derived using three measures for each class which are statistical between-class distance, interclass overlapping measure and an estimate of class impurity.…”

Section: Literature Reviewmentioning

confidence: 99%

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Density Based Feature Selection Method for Medical Datasets

M*¹,

Sarojini²

2019

IJITEE

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Abstract: High dimensional data are found in the medical domain that needs to be processed for improved data analysis. In order to deal with the curse of dimensionality, feature selection process is employed in almost all data mining applications. Dataset. The proposed feature selection method evaluates the merit of each feature, assign weights to the feature and rank the features based on their feature density. The reduced feature Network (CNN). The performance of the classification algorithms are evaluated based on the performance metrics Accuracy, Sensitivity and Specificity. Experimental results indicate that the performance of the classification algorithms SVM, Gradient Boosting, and CNN is improved after the feature selection process. In this research work, Density based Feature Selection (DFS) method that ranks the features by finding the Probability Density Function (PDF) of each feature is applied to medical datasets that suffer from the curse of dimensionality. The DFS method is a filter based approach that selects the most discriminatory features from the given feature set. The feature selection method evaluates the importance of the feature with regard to the target class using density function. The DFS method has major advantages over other methods, since it is based on the ranking method to select the most discriminatory features from the whole feature set. This research work finds the best feature subset that can be used in prediction and classification of medical datasets imbibed with high dimensionality. The DFS method based on PDF is applied on the three medical datasets namely Chronic Kidney Disease (CKD) dataset, Breast Cancer Wisconsin Dataset and Parkinsons subset is then validated by the application three classification algorithms namely Support Vector Machine (SVM), Gradient Boosting, and Convolutional Neural

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Density Based Feature Selection Method for Medical Datasets

M*¹,

Sarojini²

2019

IJITEE

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“…All the features remained from different values of k have been used in the next process. In order to assess the performance of selected features from Table 2, random forest classifier [15], [24] was utilized in our experiment. Table 3 shows the classification performance from different values of k. decreased about 0.4% and 1.3% when the k is 5 and 10 respectively.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…They evaluate their proposed method using two different domain area; image and protein interaction datasets. Due to time and space complexity, Santos et al [15] have come out with ensemble feature ranking strategy to improve the efficiency of the classification performance. They compared the performance of the proposed method with support vector machine (SVM), bagging, random forest (RF) and Naive Bayes (NB) using breast cancer dataset.…”

Section: Feature Selection Algorithmsmentioning

confidence: 99%

Feature Selection Optimization using Hybrid Relief-f with Self-adaptive Differential Evolution

Zainudin¹,

Sulaiman²,

Mustapha³

et al. 2017

IJIES

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Abstract:In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository.

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“…The results showed that BLAST with fading factors outperformed than BLAST using the window approach. Santos (20) proposed an ensemble feature ranking by using Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square and other classifiers on breast cancer dataset with the best performance of Naïve Bayes having higher AUC and lower FPR. Khan et al (21) presented a technique called as Optimal Tree Ensemble (OTE) by integrating trees that were accurate and diverse.…”

Section: Related Workmentioning

confidence: 99%

Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

2020

IJST

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Background/Objectives: Owing to the continuous increase of electronic records and recent advances in machine learning, various automated disease diagnosis tools have been developed and proposed in healthcare sector. In the present study, an ensemble methodology using voting and boosting techniques has been proposed for optimal selection of features and prediction of infants' data of India. Methods/Analysis: For feature selection, the best-first search algorithm of wrapper technique has been used in addition to votingboosting. The proposed ensemble consists of combination of heterogeneous classifiers including Random Forest, J48, JRip, CART and Stochastic Gradient Descent (SGD). The effectiveness of the proposed ensemble and single classifiers have been investigated in terms of classification accuracy, precision, f-measure, recall, MCC and PRC area using varied k-fold cross validation. Findings: The results depicted that the proposed Voting-Boosting ensemble (k=15) outperforms the individual classifiers using selected features. Applications / Improvements: The proposed Voting-Boosting ensemble can be extended by using more state-of-the art classification approaches and further utilized for other healthcare datasets for enhancing the performance.

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Ensemble Feature Ranking Applied to Medical Data

Cited by 21 publications

References 25 publications

Density Based Feature Selection Method for Medical Datasets

Density Based Feature Selection Method for Medical Datasets

Feature Selection Optimization using Hybrid Relief-f with Self-adaptive Differential Evolution

Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

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