Theoretical Basis of Novelty Detection in Time Series Using Negative Selection Algorithms

2006 ◽  
pp. 376-389 ◽  
Author(s):  
Rafał Pasek
Keyword(s):  
Time Series ◽  
Theoretical Basis ◽  
Negative Selection ◽  
Novelty Detection ◽  
Selection Algorithms

A cosine similarity-based negative selection algorithm for time series novelty detection

2006 ◽  
Vol 20 (6) ◽  
pp. 1461-1472 ◽  
Author(s):  
Yonggui Dong ◽  
Zhaoyan Sun ◽  
Huibo Jia
Keyword(s):  
Time Series ◽  
Negative Selection ◽  
Novelty Detection ◽  
Cosine Similarity ◽  
Selection Algorithm ◽  
Negative Selection Algorithm

A Biological Immunity-Inspired Novelty Detection Algorithm for Rotor System Monitoring

2005 ◽  
Vol 293-294 ◽  
pp. 71-78 ◽  
Author(s):  
Yong Gui Dong ◽  
Ensheng Dong ◽  
Huibo Jia ◽  
Wener Lv
Keyword(s):  
Time Series ◽  
Negative Selection ◽  
Time Series Data ◽  
Detection Efficiency ◽  
Novelty Detection ◽  
Rotor System ◽  
Series Data ◽  
Selection Algorithm ◽  
Detection Techniques ◽  
Negative Selection Algorithm

In case of mechanical system health monitoring, a need to develop normal-knowledge based novelty detection techniques is increasing. The negative selection algorithm, which is inspired from the operation mechanism of human immune system, is one of such approaches. Our approach is to apply the idea for the anomaly detection in the vibration time series of the rotor system. A real-valued negative selection algorithm based on Euclidean distance, as well as cosine similarity, has been implemented. By means of adding the corresponding coverage radius to each antibody elements, the detection efficiency of each antibody element is increased. The detection efficiency is evaluated with simulated data as well as vibration signal sampled from one rotor system. The results indicate that the algorithm can efficiently detect the anomaly in time series data. Moreover, the number of detectors in antibody set is less enough for potential application in online signal monitoring.

Novelty detection in a changing environment: A negative selection approach

2010 ◽  
Vol 24 (4) ◽  
pp. 1114-1128 ◽  
Author(s):  
Cecilia Surace ◽  
Keith Worden
Keyword(s):  
Negative Selection ◽  
Novelty Detection ◽  
Changing Environment ◽  
Selection Approach

A Time-Series Novelty Detection Chip for Sonar

2004 ◽  
Vol 19 (4) ◽  
Author(s):  
T.K. Horiuchi ◽  
R. Etienne-Cummings
Keyword(s):  
Time Series ◽  
Novelty Detection

A Negative Selection Algorithm Base on the Self R-Tree

2013 ◽  
Vol 411-414 ◽  
pp. 2007-2012
Author(s):  
Kun Peng Wang
Keyword(s):  
Time Complexity ◽  
Negative Selection ◽  
Selection Process ◽  
The Self ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Data Query ◽  
Query Process ◽  
Algorithm Base ◽  
Selection Algorithms

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

A Study on the Performances of Danger Theory and Negative Selection Algorithms for Mobile Spam Detection

2017 ◽  
Vol 23 (5) ◽  
pp. 4586-4590 ◽  
Author(s):  
Nurul Fadhilah Sulaiman ◽  
Mohd Zalisham Jali ◽  
Zul Hilmi Abdullah ◽  
Shaharudin Ismail
Keyword(s):  
Negative Selection ◽  
Spam Detection ◽  
Danger Theory ◽  
Selection Algorithms

Immune K-means and negative selection algorithms for data analysis

2009 ◽  
Vol 179 (10) ◽  
pp. 1407-1425 ◽  
Author(s):  
Michał Bereta ◽  
Tadeusz Burczyński
Keyword(s):  
Data Analysis ◽  
Negative Selection ◽  
Selection Algorithms

scholarly journals A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Tao Li ◽  
Xin Xiao
Keyword(s):  
Negative Selection ◽  
Artificial Immune Systems ◽  
Real Space ◽  
Time Cost ◽  
Selection Algorithm ◽  
Theory Analysis ◽  
Negative Selection Algorithm ◽  
Matching Process ◽  
Self Tolerance ◽  
Selection Algorithms

Negative selection algorithm is one of the main algorithms of artificial immune systems. However, candidate detectors randomly generated by traditional negative selection algorithms need to conduct self-tolerance with all selves in the training set in order to eliminate the immunological reaction. The matching process is the main time cost, which results in low generation efficiencies of detectors and application limitations of immune algorithms. A novel algorithm is proposed, named GB-RNSA. The algorithm analyzes distributions of the self set in real space and regards then-dimensional [0, 1] space as the biggest grid. Then the biggest grid is divided into a finite number of sub grids, and selves are filled in the corresponding subgrids at the meantime. The randomly generated candidate detector only needs to match selves who are in the grid where the detector is and in its neighbor grids, instead of all selves, which reduces the time cost of distance calculations. And before adding the candidate detector into mature detector set, certain methods are adopted to reduce duplication coverage between detectors, which achieves fewer detectors covering the nonself space as much as possible. Theory analysis and experimental results demonstrate that GB-RNSA lowers the number of detectors, time complexity, and false alarm rate.

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