Research on Color Feature Selection Using Genetic Algorithm in Robot Soccer

2012 ◽  
Vol 505 ◽  
pp. 311-316
Author(s):  
Xin Luan ◽  
Ming Chen ◽  
Zheng Yuan Sun ◽  
Da Lei Song ◽  
Lei Hua Ge ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Recognition Accuracy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Soccer Robot ◽  
Color Feature ◽  
Dichromatic Reflection ◽  
New Feature

Feature selection is a hot topic in the field of pattern recognition. In this paper, we present a new feature selection algorithm which is used on the soccer robot MT-R for the ball recognition. The illumination invariant color feature set is defined based on the dichromatic reflection. By means of genetic algorithm we determine the most discriminating color feature subset. Experimental results show that the proposed color feature subset achieves high object recognition accuracy.

New Feature Selection Method Based on SVM-RFE

2014 ◽  
Vol 926-930 ◽  
pp. 3100-3104 ◽  
Author(s):  
Xi Wang ◽  
Qiang Li ◽  
Zhi Hong Xie
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Recognition Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Testing Time ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Accuracy Rate ◽  
New Feature

This article analyzed the defects of SVM-RFE feature selection algorithm, put forward new feature selection method combined SVM-RFE and PCA. Firstly, get the best feature subset through the method of cross validation of k based on SVM-RFE. Then, the PCA decreased the dimension of the feature subset and got the independent feature subset. The independent feature subset was the training and testing subset of SVM. Make experiments on five subsets of UCI, the results indicated that the training and testing time was shortened and the recognition accuracy rate of the SVM was higher.

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

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.

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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