A comparative analysis of meta-heuristic optimization algorithms for feature selection and feature weighting in neural networks

2021 ◽  
Author(s):  
P. M. Diaz ◽  
M. Julie Emerald Jiju
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Comparative Analysis ◽  
Optimization Algorithms ◽  
Feature Weighting ◽  
Heuristic Optimization

Py_FS: A Python Package for Feature Selection Using Meta-Heuristic Optimization Algorithms

2021 ◽  
pp. 495-504
Author(s):  
Ritam Guha ◽  
Bitanu Chatterjee ◽  
S. K. Khalid Hassan ◽  
Shameem Ahmed ◽  
Trinav Bhattacharyya ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Python Package

scholarly journals Nature-Inspired Meta-heuristic Optimization Algorithms for Breast Cancer Diagnostic Model: A Comparative Study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Tinuke O Oladele ◽  
Babatunde J Olorunsola ◽  
Taye O Aro ◽  
Hakeem B Akande ◽  
Oyenike A Olukiran
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Support Vector ◽  
Diagnostic Model ◽  
Feature Subset ◽  
Selection Of

The selection of features is used to obtain a subset of features by the removal of irrelevant features with no or less predictive output. Meta-heuristic algorithms are appropriate for the selection of features because feature subset representation is direct and the evaluation is easily accomplished. This paper performed a comparative study on the impact of meta-heuristic optimization algorithms on breast cancer diagnosis using Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The two feature selection algorithms were used to obtain the relevant attributes from the Wisconsin breast cancer (original) dataset. The selected attributes were passed to seven learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naïve Bayes (NB), K Nearest Neighhood (KNN), Neural Network (NN), Logistic Regression (LR), and Random Forest (RF). The diagnostic model was evaluated based on accuracy, precision, recall, and F1-measure. Experimental showed that the highest accuracy of 97.1388% was obtained in both PSO and ACO using RF classifier, the highest precision value of 0.9720 was recorded in ACO using RF classifier,  the highest recall value of 0.9750 was achieved in PSO using RF classifier, the highest F1-measure value of 0.9700 was obtained in PSO using SVM, the highest kappa statistic of 0.9370 was obtained in both PSO and ACO using RF and the lowest time of 0s was taken to build a model was recorded in PSO using KNN and NB, and also in ACO using KNN. The paper concluded that the breast diagnostic model using PSO and ACO with different learning algorithms revealed that the accuracy of RF outperformed other algorithms. Also, it was shown that ACO produced better precision using RF compared with PSO and PSO gave better recall using RF compared with ACO, PSO recorded an efficient F1-measure using SVM. The best time used to build a model was obtained in PSO for KNN and NB, and ACO with KNN.Keywords— Breast cancer, Data mining, Diagnosis, Feature selection, Meta-heuristic. 

scholarly journals Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms

2020 ◽  
Vol 9 (2) ◽  
pp. 79-90
Author(s):  
Noor Fatima
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Deep Neural Network ◽  
Optimization Algorithms ◽  
Trial And Error ◽  
Data Scientist ◽  
Insightful Analysis ◽  
And Performance

Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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