Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

Panthong, Rattanawadee; Srivihok, Anongnart

doi:10.1016/j.procs.2015.12.117

Cited by 102 publications

(50 citation statements)

References 13 publications

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“…The commonly used classification algorithms for identifying the most relevant input variables are: Naïve Bayes (Cortizo & Giraldez, 2006, Panthong & Srivihok, 2015), SVM (Maldonado & Weber, 2009, Maldonado, Weber & Famili, 2009), Random Forest (Rodin et al, 2009), Bagging (Panthong & Srivihok, 2015), AdaBoost (Panthong & Srivihok, 2015), and Extreme Learning Machines (Benoít, Van Heeswijk, Miche, Verleysen & Lendasse, 2013). These classification techniques combined with a greedy search algorithm allow for finding the optimal number of features by iteratively selecting features based on the classifier performance (Bengio et al, 2003).…”

Section: Methodsmentioning

confidence: 99%

“…At each iteration, the Random Forest (RF) algorithm, incorporating a hierarchical decision tree structure was used to explore all possible subsets of the features and measure their importance with respect to the classification outcome (Gregorutti, Michel & Saint-Pierre, 2017). To assess the robustness of RF-RFE process in selecting optimal subset of features, we applied the RFE technique to another type of ensemble methods, namely, bootstrap aggregated (bagged) trees (BT) and compared the results (Panthong & Srivihok, 2015). Similarly, the BT-RFE performance was evaluated in a 10-fold cross-validation repeated five times with different split positions.…”

Section: Methodsmentioning

confidence: 99%

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A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

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1Computerized clinical decision support systems can help to provide objective, standardized, 2 and timely dementia diagnosis. However, current computerized systems are mainly based 3 on the group analysis, discrete classification of disease stages, or expensive and not readily 4 accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and 5 functional assessments (CFA). In this study, we developed a computational framework using 6 a suite of machine learning tools for identifying key markers in predicting the severity of 7 Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine 8 learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression 9 (SVR), and k-Nearest Neighbor (kNN reg ) for regression and Support Vector Machine (SVM), 10Random Forest (RF), and k-Nearest Neighbor (kNN class ) for classification, were used for the 11 development of predictive models. We demonstrated high predictive power of CFA. 12Predictive performance of models incorporating CFA was shown to be consistently higher 13 accuracy than those based solely on biomarker modalities. We found that KRR and SVM 14 were the best performing regression and classification methods respectively. The optimal 15 SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, 16MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the 17 best performance of the KRR model was reported with combined CFA and MRI 18 neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power 19 of CFA and their widespread use in clinical practice, we then designed a data-driven and 20 self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating 21 the severity of AD of an individual on a continuous spectrum. The system implemented an 22 automated computational approach for data pre-processing, modelling, and validation and 23 used exclusively the scores of selected cognitive measures as data entries. Taken together, 24we have developed an objective and practical CDSS to aid AD diagnosis. 25Keywords: dementia; Alzheimer's disease; decision support system; machine learning; 26 diagnosis support; cognitive impairment 27 97 CDSS for evaluating the severity of AD of an individual on a continuous spectrum. In order to 98 achieve this, we first utilize a suite of machine learning techniques to extract useful 99 information from large volumes of patient data and provide a disease outcome prediction for 100 different types and combinations of AD markers. We demonstrate that CFA can reliably and 101 accurately provide prediction of AD severity. Next, we design a CDSS that incorporates an 102 automated computational approach for data pre-processing, modelling, and validation and 103 uses selected CFA scores as data input. Since our system was designed to utilize 104 information from readily available and cost-effective CFA markers, it can be easily 105 implemented in general cli...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…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%

“…Experimental results reveal that PDF-based feature selection method has achieved better accuracy that the existing PLS-based feature selection method for early identification of AD. Rattanawadee Panthong and Anongnart Srivihok [13] have presented a wrapper based feature selection process for reducing the dimensionality in medical datasets. The methods that have been used in this work include SFS, SBS and ensemble algorithms namely Bagging and AdaBoost based on the evaluation of the subset.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Filter and Wrapper methods of FS have been applied by many researchers for classification problems to select candidate subset(reduced feature set) that increase the performance of classifiers [5 ] [ 6]. In addition to these two methods, embedded FS approach also have been applied for the classification problems as part of modeling process.…”

Section: Related Studymentioning

confidence: 99%

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

Potharaju¹,

Sreedevi²

2017

JESTR

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Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an inevitable task to shorter the number of features presented in the initial data set for better classification result, minimized computation time, and reduced memory consumption. In this article, a novel framework using Correlation Coefficient (CCE) and Symmetrical Uncertainty (SU) for selecting the subset of feature is proposed. The selected features are congregated into finite number of clusters by grading their CCE and comparing the SU values. In each cluster, a feature with maximum SU value is retained while remaining features in the same cluster are ignored. The proposed framework was examined with Ten(10) real time benchmark data sets. Experimental outcomes show that the proposed method is outruns than majority of conventional feature selection methods(Information Gain, Chi-Square, Gain Ratio, ReliefF) in accuracy. This method is tested using Tree Based, Rule Based, Lazy, and Naive Bayes learners.

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Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

Cited by 102 publications

References 13 publications

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Density Based Feature Selection Method for Medical Datasets

A Novel Clustering Based Candidate Feature Selection Framework Using Correlation Coefficient for Improving Classification Performance

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