A Novel Multi-Objective Forest Optimization Algorithm for Wrapper Feature Selection

2021 ◽  
pp. 114737
Author(s):  
Babak Nouri-Moghaddam ◽  
Mehdi Ghazanfari ◽  
Mohammad Fathian
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Multi Objective ◽  
Wrapper Feature Selection

scholarly journals Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 76333-76356 ◽  
Author(s):  
Ali Muhammad Usman ◽  
Umi Kalsom Yusof ◽  
Syibrah Naim
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Cuckoo Optimization Algorithm ◽  
Multi Objective

Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

2021 ◽  
pp. 384-401
Author(s):  
Waheed Ali H. M. Ghanem ◽  
Yousef A. Baker El-Ebiary ◽  
Mohamed Abdulnab ◽  
Mohammad Tubishat ◽  
Nayef A. M. Alduais ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Multi Objective ◽  
Neural Network Classification ◽  
Wrapper Feature Selection

scholarly journals A Novel Multi-Objective Moth-Flame Optimization Algorithm for Feature Selection

2018 ◽  
Vol 11 (38) ◽  
pp. 1-13
Author(s):  
M. A. Soliman Ghada ◽  
H. M. Abou-El-Enien Tarek ◽  
E. Emary ◽  
M. H. Khorshid Motaz ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Multi Objective ◽  
Moth Flame Optimization Algorithm

scholarly journals An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

2021 ◽  
Vol 11 (21) ◽  
pp. 10237
Author(s):  
Thaer Thaher ◽  
Atef Zaguia ◽  
Sana Al Azwari ◽  
Majdi Mafarja ◽  
Hamouda Chantar ◽  
...  
Keyword(s):  
Feature Selection ◽  
Performance Prediction ◽  
Optimization Algorithm ◽  
Transfer Functions ◽  
Chaotic Map ◽  
Imbalanced Data ◽  
Whale Optimization Algorithm ◽  
Linear Discriminant ◽  
Whale Optimization ◽  
Wrapper Feature Selection

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

scholarly journals Study on Multi-Objective Bio-Inspired Algorithms for Feature Selection

2021 ◽  
pp. 5-8
Author(s):  
Rachna Kulhare ◽  
Dr. S. Veenadhari ◽  
Neha Sharma
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Multi Objective ◽  
Selection For ◽  
Data Optimization

With the era of big data, the problems of data size and data optimization have become more diversified and complicated, thus the optimization method has become the focus of people's attention. Algorithm is used to solve practical problems in various fields. In this paper, we studied different techniques of feature selection for big data using optimization algorithm.

Sign in / Sign up

Export Citation Format

Share Document