Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database

Dash, Ch. Sanjeev Kumar; Saran, Amitav; Sahoo, Pulak; Dehuri, Satchidananda; Cho, Sung-Bae

doi:10.1016/j.patrec.2016.05.002

Cited by 14 publications

(6 citation statements)

References 41 publications

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“…The parameters of multi-layer perceptron (MLP) along with training algorithms and Simple Logistic are defined as prescribed in [3].…”

Section: Description Of Datasets and Parametersmentioning

confidence: 99%

“…Therefore, to derive novel and useful results for the decision maker, the process of imputing and identifying missing values and relevant features, respectively are highly recommended. Since decades ago these two problems are treated as the problem of importance in object detection & recognition (pattern recognition) [11] and data mining [3] in general and ECG signals diagnosis [13], power flow calculation [14], simulation and control of dynamic system [15], magnetic modeling [16], identification and classification of plant leaf diseases [17], discrimination of low and full fat Yogurts [19,20,22] in specific.…”

Section: Introductionmentioning

confidence: 99%

“…There are several approaches to impute missing values of which we concentrate on least square estimation method [2,3]. A large variety of feature selection techniques have been developed under the umbrella of filter, wrapper, and embedded methods with a goal to select relevant subset of features [4].…”

Section: Introductionmentioning

confidence: 99%

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Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

Dash¹,

Behera²,

Dehuri

et al. 2020

ACI

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This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

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“…The parameters of multi-layer perceptron (MLP) along with training algorithms and Simple Logistic are defined as prescribed in [3].…”

Section: Description Of Datasets and Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

Dash¹,

Behera²,

Dehuri

et al. 2020

ACI

Self Cite

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show abstract

“…The output weight has not been considered in the feature extraction process by using the proposed algorithm [7]. Dash et al used differential evolution (DE) to optimize RBFNN by adaptively controlling the hidden parameter in the hidden layers [8]. This method, nevertheless, lacks interpretability in the hidden layer of RBFNN [8].…”

Section: Introductionmentioning

confidence: 99%

“…Dash et al used differential evolution (DE) to optimize RBFNN by adaptively controlling the hidden parameter in the hidden layers [8]. This method, nevertheless, lacks interpretability in the hidden layer of RBFNN [8]. Yang and Ma [9] tried to apply the Sparse Neural Network (SNN) algorithm to optimize the hidden neuron number.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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On the impact of information-sharing model between subpopulations in the Island-based evolutionary algorithms: search manager framework as a case study

Abdi

Asadpour

2023

J Supercomput

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Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database

Cited by 14 publications

References 41 publications

Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

On the impact of information-sharing model between subpopulations in the Island-based evolutionary algorithms: search manager framework as a case study

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