Extreme Learning Machine for two category data classification

Subbulakshmi, C.V.; Deepa, S. N.; Malathi, N.

doi:10.1109/icaccct.2012.6320822

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…(1) The proposed ensemble outperforms the individual and ensemble classifiers in all three data sets which contain different feature spaces, which means that its generalization ability is outstanding. In contrast, most previous studies used only one data set [17,18,54], and that weakened the persuasive power of their results.…”

Section: Discussionmentioning

confidence: 93%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang²

2020

Preprint

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Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

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Section: Discussionmentioning

confidence: 93%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang²

2020

Preprint

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“…Buscema et al [19] used training with input selection and testing (TWIST) algorithm for FS. Finally, Subbulakshmi et al [20] used extreme learning machine (ELM) to select the most relevant fewer features from the Stat log dataset. But the choice of classifier affects the performance of the prediction accuracy.…”

Section: Fig 7 Accuracy Of Echocardiogram Dataset Using Different Cmentioning

confidence: 99%

Cuckoo inspired algorithms for feature selection in heart disease prediction

Usman

Yusof

Naim

2018

Int. J. Adv. Intell. Informatics

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Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.

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“…Gokulnath and Shantharajah [17] proposed a classification model based on genetic algorithm (GA) and support vector machine (SVM), obtaining an accuracy of 88.34% on Cleveland heart disease dataset. Subbulakshmi et al [18] performed a detailed analysis of different activation functions of extreme learning machine (ELM) using Statlog heart disease dataset. The results indicated that ELM achieved an accuracy of 87.5% , higher than other methods.…”

Section: Introductionmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang

2021

BMC Med Inform Decis Mak

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Background Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

show abstract

Extreme Learning Machine for two category data classification

Cited by 22 publications

References 10 publications

A hybrid cost-sensitive ensemble for heart disease prediction

A hybrid cost-sensitive ensemble for heart disease prediction

Cuckoo inspired algorithms for feature selection in heart disease prediction

A hybrid cost-sensitive ensemble for heart disease prediction

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