Comparing of deep neural networks and extreme learning machines based on growing and pruning approach

Akyol, Kemal

doi:10.1016/j.eswa.2019.112875

Cited by 47 publications

(11 citation statements)

References 28 publications

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“…In general, the proposed model has outperformed all previous study results applied on the same dataset such as ANN [4], KNN [5], NB [6], SMOTE + XGBoost [7], FNN [8], DNN [9], and hybrid autoencoder [10]. The best accuracy for multi-class classification problem on the SCADI dataset still goes for DNN [9]; however, it should be noted that they used hold-out validation method (60%/40% for training and testing) which is less reliable and increase the possibility of over-fitting and over-optimism, as compared with 10-fold cross-validation [47]. Finally, in term of binary-class classification problem, our proposed model has the highest performance by achieving the accuracy up to 98.57% as compared with the results from previous related works.…”

Section: Comparison Of the Proposed Model With Previous Workmentioning

confidence: 66%

“…Fuzzy neural networks (FNN) were reported as the best classifier, providing 85.11% test accuracy on the binary class of SCADI dataset. Most recently, Akyol (2020) evaluated the performance of deep neural networks (DNN) and extreme learning machines (ELM) on the multi-class SCADI dataset [9]. The study used the hold-out method to divide the data into 60% and 40% for training and testing data, respectively.…”

Section: Self-care Prediction Based On Icf-cy Datasetmentioning

confidence: 99%

“…This framework has been used as a standardized guideline to classify self-care problems [3]. Several previous studies have utilized machine learning algorithms, such as the artificial neural network (ANN) [4], k-nearest neighbor (KNN) [5], naïve Bayes (NB) [6], extreme gradient boosting (XGBoost) [7], fuzzy neural networks (FNN) [8], deep neural networks (DNN) [9], and hybrid autoencoder [10] to help occupational therapists improve the classification accuracy and reducing the time as well as the cost of self-care classification [11].…”

Section: Introductionmentioning

confidence: 99%

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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Section: Comparison Of the Proposed Model With Previous Workmentioning

confidence: 66%

Section: Self-care Prediction Based On Icf-cy Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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“…Similarly, the prediction by our algorithm was compared with that of Kemal Akyol's research [11] (Tables 4-6).…”

Section: Comparative Analysismentioning

confidence: 99%

“…Navas et al [10] applied different artificial neural networks (ANNs) and empirical classification regression models to predict wind speed, and estimated the wind speed using the SPSS software. Using the growing and pruning method, Akyol [11] determined the ideal parameters (i.e. the optimal number of hidden layers, the optimal number of hidden layer neurons, and the activation function) of deep NNs and extreme learning machines, and verified the prediction ability of the designed model.…”

Section: Introductionmentioning

confidence: 99%

Design of Improved Algorithm and Model for Multi-Constrained Fuzzy Predictive Analysis

Song¹,

Xie²,

Shi³

2019

RIA

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Complex system engineering often has high fuzziness and multiple constraints. In this case, it is difficult to achieve consistent results through predictive analysis. To solve the problem, this paper explores the key techniques and methods for predictive analysis on complex systems, and puts forward an improved strategy for multi-constrained fuzzy predictive analysis. The author explained the normalization, weighting, granularity setting and classic domain of the attributes of multiple constraints, introduced the calculation of the fuzzy distance and fuzzy closeness for the attributes of multiple constraints, and detailed the realization of our algorithm and model multi-constrained fuzzy predictive analysis. The effectiveness and feasibility of our algorithm and model were demonstrated through comparison with relevant data in the literature. The results show that the results of our approach agree with those of the literature. The proposed algorithm and model provide a good theoretical basis to predictive analysis of complex system engineering, especially that with multiple fuzzy constraints.

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Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children

Putatunda

2020

Neural Comput & Applic

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Comparing of deep neural networks and extreme learning machines based on growing and pruning approach

Cited by 47 publications

References 28 publications

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Design of Improved Algorithm and Model for Multi-Constrained Fuzzy Predictive Analysis

Care2Vec: a hybrid autoencoder-based approach for the classification of self-care problems in physically disabled children

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