Dendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification

Xu, Zhe; Wang, Ziqian; Li, Jiayi; Jin, Ting; Meng, Xianglian; Gao, Shangce

doi:10.1016/j.knosys.2021.107536

Cited by 34 publications

(16 citation statements)

References 66 publications

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“…Artificial neural networks (ANN) have achieved great success in many realistic optimization fields [82][83][84]. The dendritic neural model (DNM) is a new model that takes into account the nonlinear information processing capability of dendrites [85,86]. This neural model has the ability to prune dendrites and synapses and to detect the neural morphology of the task; it has been shown to be effective in classification problems [87].…”

Section: Discussion Of the Parameter µmentioning

confidence: 99%

An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Yang

Liu³

et al. 2022

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Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed.

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Section: Discussion Of the Parameter µmentioning

confidence: 99%

An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Yang

Liu³

et al. 2022

Symmetry

Self Cite

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“…The method found better than the methods considered for comparison and able to reduce the redundant synapses and dendritic layers. In a short period of time, a few evolutionary algorithms for DNM parameter tuning are found in the literature such as GA [26], biogeography-based optimization [27], social learning PSO [28], DE [29,30], cuckoo search [31], near population size reduction [32], whale optimization [33], and particle swarm optimization [34,35], etc. These hybrid DNMs are used for classification and forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

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Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: (1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA); (2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

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“…The concept of the nervous activity character in mathematics was first introduced in 1943 by McCulloch and Pitts [1]. They claimed that the nervous activity could be formulated mathematically due to the "all-or-none" hypothesis associated with propositional logic.…”

Section: Introductionmentioning

confidence: 99%

Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

Mudeng¹,

Jeong²,

Choe³

2022

Computers, Materials &Amp; Continua

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A lump growing in the breast may be referred to as a breast mass related to the tumor. However, not all tumors are cancerous or malignant. Breast masses can cause discomfort and pain, depending on the size and texture of the breast. With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant. With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images. This work accomplished breast masses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets. A residual neural network 50 (ResNet50) model along with an adaptive gradient algorithm, adaptive moment estimation, and stochastic gradient descent optimizers, as well as data augmentations and fine-tuning methods, were implemented. In addition, a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models. The results of training accuracy, p-value, test accuracy, area under the curve, sensitivity, precision, F1-score, specificity, and kappa for adaptive gradient algorithm 25%, 75%, 100%, and stochastic gradient descent 100% fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.

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Dendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification

Cited by 34 publications

References 66 publications

An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

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