scholarly journals S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem

2021 ◽  
Author(s):  
Kushal Kanti Ghosh ◽  
Ritam Guha ◽  
Suman Kumar Bera ◽  
Neeraj Kumar ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Transfer Functions ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Manta Ray

Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem

2020 ◽  
Vol 09 (01) ◽  
pp. 1950021
Author(s):  
Rahul Hans ◽  
Harjot Kaur
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Transfer Functions ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Ant Lion Optimization ◽  
Ant Lion Optimizer ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Better Than

These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.

scholarly journals S-Shaped versus V-Shaped Transfer Functions for Binary Manta Ray Foraging Optimization in Feature Selection Problem

2020 ◽  
Author(s):  
Kushal Kanti Ghosh ◽  
Ritam Guha ◽  
Suman Kumar Bera ◽  
Neeraj Kumar ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Transfer Functions ◽  
Search Space ◽  
Optimization Techniques ◽  
Classification Model ◽  
Feature Selection Problem ◽  
The Core ◽  
Core Concepts ◽  
Manta Ray ◽  
State Of Art

Abstract Feature selection (FS) is considered as one of the core concepts in the areas of machine learning and data mining which immensely impacts the performance of classification model. Through FS, irrelevant or partially relevant features can be eliminated which in turn helps in enhancing the performance of the model. Over the years, researchers have applied different meta-heuristic optimization techniques for the purpose of FS as these overcome the limitations of traditional optimization approaches. Going by the trend, we introduce a new FS approach based on a recently proposed meta-heuristic algorithm called Manta Ray Foraging Optimization (MRFO) which is developed following the food foraging nature of the Manta rays, one of the largest known marine creatures. As MRFO is apposite for continuous search space problems, we have adapted a binary version of MRFO to t it into the problem of FS by applying eight different transfer functions belonging to two different families: S-shaped and V-shaped. We have evaluated the eight binary versions of MRFO on 18 standard UCI datasets. Of these, the best one is considered for comparison with 16 recently proposed meta-heuristic FS approaches. The results show that MRFO outperforms the state-of-art methods in terms of both classification accuracy and number of features selected.

scholarly journals The GRA-two algorithm for massive-scale feature selection problem in power system scenario classification and prediction

2021 ◽  
Vol 7 ◽  
pp. 293-303
Author(s):  
Yang Wang ◽  
Xinxiong Jiang ◽  
Faqi Yan ◽  
Yu Cai ◽  
Siyang Liao
Keyword(s):  
Feature Selection ◽  
Power System ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Scale Feature ◽  
Massive Scale

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

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