scholarly journals A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection

2008 ◽  
Vol 20 (7) ◽  
pp. 868-879 ◽  
Author(s):  
Weiguo Sheng ◽  
Xiaohui Liu ◽  
M. Fairhurst
Keyword(s):  
Feature Selection ◽  
Memetic Algorithm

Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

2019 ◽  
Vol 78 (18) ◽  
pp. 25753-25779 ◽  
Author(s):  
Manosij Ghosh ◽  
Tuhin Kundu ◽  
Dipayan Ghosh ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Memetic Algorithm ◽  
Facial Emotion Recognition ◽  
Hill Climbing ◽  
Facial Emotion ◽  
Selection For

Memetic algorithm based feature selection for hyperspectral images classification

2017 ◽  
Author(s):  
Mingyang Zhang ◽  
Jingjing Ma ◽  
Maoguo Gong ◽  
Hao Li ◽  
Jia Liu
Keyword(s):  
Feature Selection ◽  
Memetic Algorithm ◽  
Hyperspectral Images ◽  
Selection For

scholarly journals ANALYSIS OF CRONA VIRUS (COVID-19) USING MEMETIC ALGORITHM BASED FEATURE SELECTION AND FEED FORWARD NEURAL NETWORKS FOR CLASSIFIER

2021 ◽  
Vol 9 (1) ◽  
pp. 1413-1420
Author(s):  
G. Mahalakshmi, P. Santhi
Keyword(s):  
Feature Selection ◽  
Mortality Rate ◽  
Memetic Algorithm ◽  
World Health ◽  
Feed Forward Neural Network ◽  
Human Mortality ◽  
Feed Forward ◽  
The People ◽  
First Case ◽  
The World

Coronavirus disease (COVID -19) is the most pandemic disease in the world. Due to this virus, most of the humans are lost their life. It increases the human mortality rate and minimizes the economic rate of the country. As per the World Health Organization (WHO) report, the first case is reported on 31 December 2019 from Wuhan city in china. After that, the affected rate is rapidly increasing and most of the cases are leads to death.  At present, 215 countries have affected by this pandemic disease. As per WHO, COVID-19 has infected 3,557,235 people so far. So, the analysis and the prediction of COVID-19 is a very important task to give the awareness among the people for reducing the human mortality rate and also the affected rate of the people. This paper gives the analysis of COVID-19 using Memetic algorithm for feature selection and Feed Forward Neural Network Classifier for classification. The constructed model gives the better performance with low error rate.

A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection

2014 ◽  
Vol 22 (1) ◽  
pp. 1-45 ◽  
Author(s):  
Nicolás García-Pedrajas ◽  
Aida de Haro-García ◽  
Javier Pérez-Rodríguez
Keyword(s):  
Feature Selection ◽  
Memetic Algorithm ◽  
Fitness Function ◽  
Large Datasets ◽  
Support Vector ◽  
Instance Selection ◽  
Huge Amount ◽  
Explanatory Variables ◽  
Vector Machines ◽  
Testing Error

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly harms classification or recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is largely improved when the complexity of the data is reduced. One of the approaches to address problems that have too many features or instances is feature or instance selection, respectively. Although most methods address instance and feature selection separately, both problems are interwoven, and benefits are expected from facing these two tasks jointly. This paper proposes a new memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection. The proposed method performs four different local search procedures with the aim of obtaining the most relevant subsets of instances and features to perform an accurate classification. A new fitness function is also proposed that enforces instance selection but avoids putting too much pressure on removing features. We prove experimentally that this fitness function improves the results in terms of testing error. Regarding the scalability of the method, an extension of the stratification approach is developed for simultaneous instance and feature selection. This extension allows the application of the proposed algorithm to large datasets. An extensive comparison using 55 medium to large datasets from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 30 large problems, with very good results. The accuracy of the method for class-imbalanced problems in a set of 40 datasets is shown. The usefulness of the method is also tested using decision trees and support vector machines as classification methods.

Feature Selection for Handwritten Word Recognition Using Memetic Algorithm

2018 ◽  
pp. 103-124 ◽  
Author(s):  
Manosij Ghosh ◽  
Samir Malakar ◽  
Showmik Bhowmik ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Feature Selection ◽  
Word Recognition ◽  
Memetic Algorithm ◽  
Handwritten Word Recognition ◽  
Selection For

A memetic algorithm with support vector machine for feature selection and classification

2015 ◽  
Vol 7 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Messaouda Nekkaa ◽  
Dalila Boughaci
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Memetic Algorithm ◽  
Support Vector
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