Authors:
Victor Shcherbinin
and
Valery Kostenko
Affiliation:
Moscow State University, Russian Federation
Keyword(s):
Genetic Algorithm, Clustering, Machine Learning, Supervised Learning, Unsupervised Learning, Dynamic System, Abnormal Behavior, Training Set, Algebraic Approach, Axiomatic Approach.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
We consider the problem of automatic construction of algorithms for recognition of abnormal behavior segments
in phase trajectories of dynamic systems. The recognition algorithm is trained on a set of trajectories
containing normal and abnormal behavior of the system. The exact position of segments corresponding to abnormal
behavior in the trajectories of the training set is unknown. To construct recognition algorithm, we use
axiomatic approach to abnormal behavior recognition. In this paper we propose a novel two-stage training algorithm
which uses ideas of unsupervised learning and evolutonary computation. The results of experimental
evaluation of the proposed algorithm and its variations on synthetic data show statistically significant increase
in recognition quality for the recognizers constructed by the proposed algorithm compared to the existing
training algorithm.