Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification

2021 ◽  
pp. 2140007
Author(s):  
Rahul Hans ◽  
Harjot Kaur
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Breast Density ◽  
Texture Features ◽  
Mammographic Breast Density ◽  
Root Cause ◽  
Optimal Subset ◽  
The Right ◽  
Feature Selection Techniques ◽  
Aided Diagnosis

It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.

scholarly journals A Survey on Feature Selection Techniques using Evolutionary Algorithms

2021 ◽  
pp. 2796-2812
Author(s):  
Nishath Ansari
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Machine Learning Algorithms ◽  
Nsga Ii ◽  
Feature Selection Technique ◽  
Heuristic Strategies ◽  
Real World Applications ◽  
Heuristic Strategy ◽  
Feature Selection Techniques ◽  
Straightforward Approach

     Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the preferred feature matters in complex and massive data. Here, furthermore, I discuss algorithms like the genetic algorithm (GA), the Non-Dominated Sorting Genetic Algorithm (NSGA-II), Particle Swarm Optimization (PSO), and some other meta-heuristic strategies for considering the provisional separation of issues. A comparison of these algorithms has been performed; the results show that the feature selection technique benefits machine learning algorithms by improving the performance of the algorithm. This paper also presents various real-world applications of using feature selection.

A Hybrid Feature Selection Based on Mutual Information and Genetic Algorithm

2017 ◽  
Vol 7 (1) ◽  
pp. 214
Author(s):  
Yuan-Dong Lan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mutual Information ◽  
Learning Algorithm ◽  
Selection Process ◽  
Optimal Subset ◽  
Benchmark Datasets ◽  
Two Phases ◽  
Feature Dimension ◽  
Necessary And Sufficient

Feature selection aims to choose an optimal subset of features that are necessary and sufficient to improve the generalization performance and the running efficiency of the learning algorithm. To get the optimal subset in the feature selection process, a hybrid feature selection based on mutual information and genetic algorithm is proposed in this paper. In order to make full use of the advantages of filter and wrapper model, the algorithm is divided into two phases: the filter phase and the wrapper phase. In the filter phase, this algorithm first uses the mutual information to sort the feature, and provides the heuristic information for the subsequent genetic algorithm, to accelerate the search process of the genetic algorithm. In the wrapper phase, using the genetic algorithm as the search strategy, considering the performance of the classifier and dimension of subset as an evaluation criterion, search the best subset of features. Experimental results on benchmark datasets show that the proposed algorithm has higher classification accuracy and smaller feature dimension, and its running time is less than the time of using genetic algorithm.

Review of Ensemble-Based Filter Feature Selection Techniques for Building Intrusion Detection System

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Ishita Karna ◽  
Aniket Madam ◽  
Chinmay Deokule ◽  
Rahul Adhao ◽  
Vinod Pachghare
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Detection System ◽  
Critical Role ◽  
Monitoring Network ◽  
Detection Systems ◽  
Computational Overhead ◽  
Optimal Subset ◽  
Feature Selection Techniques

Intrusion detection systems (IDS) play a critical role in network security by monitoring network traffic for malicious activities and detecting vulnerability exploits against target applications or computers. A large number of redundant and irrelevant features increase the dimensionality of the dataset, which increases the computational overhead on the system and reduces its performance. This paper studies different filter-based feature selection techniques to improve performance of system. Feature selection techniques are used to select a well performing subset of features followed by technique of ensemble learning, which selects an optimal subset of features by combining multiple subsets of features. Feature selection combined with ensemble learning is explored in this paper. The performance of the algorithms implemented in existing research in terms of accuracy, false alarm rates, and true positive rates is explored, and their shortcomings are observed.

scholarly journals General practitioners’ (GPs) understanding and views on breast density in Australia: a qualitative interview study

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e047513
Author(s):  
Brooke Nickel ◽  
Hankiz Dolan ◽  
Stacy Carter ◽  
Nehmat Houssami ◽  
Meagan Brennan ◽  
...  
Keyword(s):  
General Practitioners ◽  
Breast Density ◽  
Population Based ◽  
Mammographic Breast Density ◽  
Framework Analysis ◽  
Telephone Interviews ◽  
Qualitative Interview Study ◽  
Education Support ◽  
The Right ◽  
Training And Support

ObjectivesTo understand general practitioners’ (GPs’) awareness and knowledge of mammographic breast density (BD) and their perspectives around information and potential notification of BD for women.DesignQualitative study using semistructured telephone interviews. Interviews were audiorecorded, transcribed and analysed using framework analysis.SettingAustralia.ParticipantsAustralian GPs (n=30).ResultsGPs had limited knowledge of BD and little experience discussing BD with women. There were mixed views on notification of BD with some GPs believing this information would help informed decision making about breast health and that women have the right to know any information about their bodies. While others were concerned about causing unnecessary anxiety and were worried about the uncertainty about what to advise women to do with this information, particularly in relation to supplemental breast screening. The need for an equitable system where all women are either notified or not, and also provided with publicly funded supplemental screening was raised by GPs. Overall, there was high interest in education, training and support around the topic of BD.ConclusionsAustralian GPs require education, support and evidence-based guidelines to have discussions with women with dense breasts and help manage their risk, especially if widespread notification is to be introduced in population-based screening programmes.

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