Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

2020 ◽  
Author(s):  
Esra Sarac Essiz ◽  
Murat Oturakci
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Traditional Methods ◽  
Bee Colony ◽  
Nature Inspired Algorithm

Abstract As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

A Feature Selection Method for Improved Clonal Algorithm Towards Intrusion Detection

2016 ◽  
Vol 30 (05) ◽  
pp. 1659013 ◽  
Author(s):  
Chunyong Yin ◽  
Luyu Ma ◽  
Lu Feng
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
False Positive ◽  
False Positive Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Positive Rate ◽  
Better Than

Intrusion detection is a kind of security mechanism which is used to detect attacks and intrusion behaviors. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we proposed a feature selection method for improved clonal algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and clones them. Experimental results show that the feature selection algorithm is better than the traditional feature selection algorithm on the different classifiers, and it is shown that the final detection results are better than traditional clonal algorithm with 99.6% accuracy and 0.1% false positive rate.

MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation

2020 ◽  
Vol 59 (04/05) ◽  
pp. 151-161
Author(s):  
Yuchen Fei ◽  
Fengyu Zhang ◽  
Chen Zu ◽  
Mei Hong ◽  
Xingchen Peng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Nasopharyngeal Carcinoma ◽  
Soft Tissues ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Tumor Margins

Abstract Background An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task. Objectives The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility. Methods In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure). Results To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images. Conclusion The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.

Flow Feature Selection Method Based on Statistics

2014 ◽  
Vol 1030-1032 ◽  
pp. 1709-1712
Author(s):  
Kai Min Song ◽  
Xun Yi Ren
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Experimental Results ◽  
Identification Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Statistical Feature ◽  
Flow Feature

Through the research on the flow identification algorithm based on statistical feature, this paper puts forward the statistical feature selection algorithm in order to reduce the number of features in identification, increase the speed of the flow identification, the experimental results show that the algorithm can effectively reduce the amount of features, improve the efficiency of identification.

scholarly journals Correlation based Ensemble Feature Selection Algorithm for Diagnosis of Diabetics

2020 ◽  
Vol 9 (3S) ◽  
pp. 373-373
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Correlated Data ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Highly Correlated ◽  
The Individual ◽  
Entire Dataset ◽  
Medical Dataset

One of the preprocessing steps is data cleaning and feature selection in data mining. Feature selection has more efficiency regarding dimensionality reduction, eliminating irrelevant data, improving the accuracy and enhancing the output comprehensibility. This paper utilizes wrapper / hybridfilter based feature selection method for feature selection and extraction from medical dataset. From the extracted information, the individual features are evaluated by calculating a rank value where it helps to choose highly correlated data from the entire dataset. Selected features are classified using the popular C4.5 classifier. To experiment the proposed method, the benchmark dataset is obtained from the UCI repository. It is a famous machine learning repository used by several earlier research works to evaluate the performance of their proposed methods. Finally, the accuracy of the classification method shows that our proposed method outperforms than the existing methods

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