Open AccessPosted Content
A Novel Community Detection Based Genetic Algorithm for Feature Selection
TL;DR: The authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection and indicate that the new approach continuously yields improved classification accuracy.
read more
Abstract: The selection of features is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. The findings indicate that the new approach continuously yields improved classification accuracy.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Figures
Figure 4: Average classification accuracy over all datasets on the SVM classifier 
Figure 5: Average classification accuracy over all datasets on the AdaBoost classifier 
Figure 3: Average classification accuracy over all datasets on the KNN classifier. 
Fig 2. Details of repair operation of the proposed method 
Table 3: Average classification accuracy rate and as standard deviation (shown in parenthesis) over ten runs of the evolutionary-based feature selection methods using KNN, SVM, and AdaBoost classifier. The best result is indicated in boldface and underlined, and the second-best is in boldface. 
Fig 6. Comparison of Convergence Process of Proposed Method Using Repair Operator (CDGAFS) and Without Using Repair Operator (GAFS) on SpamBase dataset
Citations
A new link prediction in multiplex networks using topologically biased random walks
TL;DR: The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW).
87
Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio
TL;DR: In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), CNN, Convolutional Auto-Encoder Neural Network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19.
63
Distributed denial of service attack prediction: Challenges, open issues and opportunities
TL;DR: In this article , the authors present the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction and highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks.
61
A Hybrid Method for Recommendation Systems based on Tourism with an Evolutionary Algorithm and Topsis Model
TL;DR: A new approach of recommendation systems in the tourism industry by a combination of the Artificial Bee Colony (ABC) algorithm and Fuzzy TOPSIS is proposed in the present paper.
60
References
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Using mutual information for selecting features in supervised neural net learning
TL;DR: This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier.