Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Emary, Eid; Zawbaa, Hossam M.; Groşan, Crina

doi:10.1109/tnnls.2016.2634548

Cited by 160 publications

(59 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another different approach was used in [40], where researchers used the Minimum Redundancy Maximum Relevance (MRMR) initialization which combines relevance with the target class and redundancy to other features.…”

Section: Initialize Populationmentioning

confidence: 99%

See 1 more Smart Citation

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

show abstract

Section: Initialize Populationmentioning

confidence: 99%

“…Composed weighted fitness function is also used in the GWO algorithm [40], which comprises an error rate and a number of selected features.…”

Section: Fitness Functionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

View full text Add to dashboard Cite

show abstract

“…Because gradient descent as a part of the BP algorithm can be easily trapped into the local minimum, GOA is employed as a stochastic algorithm to escape the local minima . Figure shows the architecture of the GOA‐based CNN.…”

Section: Segmentation By Goa‐based Cnnmentioning

confidence: 99%

Deep learning and optimization algorithms for automatic breast cancer detection

Sha

Hu²,

Rouyendegh

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-ofthe-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. K E Y W O R D S breast cancer, convolutional neural networks, feature extraction, feature selection, grasshopper optimization algorithm, image classification, image segmentation

show abstract

“…Where t is the iteration number and Max iter is the total number of iteration allowed for the optimization [9]. The pseudo code of the GWO algorithm is displayed in igure 1.…”

Section: Mathematical Modellingmentioning

confidence: 99%

“…At any given time, all solutions are at a corner of a hypercube and the solutions are grouped in binary form. Based on the basic GWO algorithm, the given wolf positions are updated, a binary restriction must be maintained according to equation (9).…”

Section: Binary Gray Wolf Optimization (Bgwo)mentioning

confidence: 99%

Untitled

2020

Ann Proteom Bioinform

View full text Add to dashboard Cite

In the classifi cation of cancer data sets, we note that they contain a number of additional features that infl uence the classifi cation accuracy. There are many evolutionary algorithms that are used to defi ne the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space. In this paper, a method of feature selection was proposed through two consecutive stages in the fi rst stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size. In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specifi c number of features affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BGWO, describes effi ciency and effectiveness by obtaining a higher classifi cation accuracy and a small number of selected genes compared to other competitor algorithms.How to cite this article: Noori NM, Qasim OS. Improving cancer diseases classifi cation using a hybrid fi lter and wrapper feature subset selection. Ann Proteom Bioinform. 2020; 4: 006-0011.

show abstract

Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Cited by 160 publications

References 26 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

Deep learning and optimization algorithms for automatic breast cancer detection

Untitled

Contact Info

Product

Resources

About