scholarly journals A new feature selection algorithm for two-class classification problems and application to endometrial cancer

2012 ◽  
Author(s):  
M. Eren Ahsen ◽  
Nitin K. Singh ◽  
Todd Boren ◽  
M. Vidyasagar ◽  
Michael A. White
Keyword(s):  
Feature Selection ◽  
Endometrial Cancer ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Classification Problems ◽  
New Feature

scholarly journals Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 144 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Detection System ◽  
Detection Performance ◽  
Cyber Attacks ◽  
Raspberry Pi ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
New Feature ◽  
Iot Devices

The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance.

BoostFS: A Boosting-Based Irrelevant Feature Selection Algorithm

2015 ◽  
Vol 29 (07) ◽  
pp. 1551011 ◽  
Author(s):  
Qi-Guang Miao ◽  
Ying Cao ◽  
Jian-Feng Song ◽  
Jiachen Liu ◽  
Yining Quan
Keyword(s):  
Feature Selection ◽  
Learning Process ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Classification Problems ◽  
Sample Distribution ◽  
Irrelevant Feature ◽  
Training Samples ◽  
Synthetic Datasets ◽  
Original Feature

In a learning process, features play a fundamental role. In this paper, we propose a Boosting-based feature selection algorithm called BoostFS. It extends AdaBoost which is designed for classification problems to feature selection. BoostFS maintains a distribution over training samples which is initialized from the uniform distribution. In each iteration, a decision stump is trained under the sample distribution and then the sample distribution is adjusted so that it is orthogonal to the classification results of all the generated stumps. Because a decision stump can also be regarded as one selected feature, BoostFS is capable to select a subset of features that are irrelevant to each other as much as possible. Experimental results on synthetic datasets, five UCI datasets and a real malware detection dataset all show that the features selected by BoostFS help to improve learning algorithms in classification problems, especially when the original feature set contains redundant features.

A new feature selection algorithm based on binomial hypothesis testing for spam filtering

2011 ◽  
Vol 24 (6) ◽  
pp. 904-914 ◽  
Author(s):  
Jieming Yang ◽  
Yuanning Liu ◽  
Zhen Liu ◽  
Xiaodong Zhu ◽  
Xiaoxu Zhang
Keyword(s):  
Feature Selection ◽  
Hypothesis Testing ◽  
Spam Filtering ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
New Feature
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