Authors:
Shakhnaz Akhmedova
;
Eugene Semenkin
and
Vladimir Stanovov
Affiliation:
Reshetnev Siberian State University of Science and Technology, Russian Federation
Keyword(s):
Bio-inspired Algorithms, Fuzzy Controller, Support Vector Machines, Semi-Supervised Learning, Classification, Constrained Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Control
;
Fuzzy Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention
in machine learning. Semi-Supervised Support Vector Machines (SVM) are based on applying the margin
maximization principle to both labelled and unlabelled examples. A new collective bionic algorithm,
namely fuzzy controlled cooperation of biology related algorithms (COBRA-f), which solves constrained
optimization problems, has been developed for semi-supervised SVM design. Firstly, the experimental
results obtained by the two types of fuzzy controlled COBRA are presented and compared and their
usefulness is demonstrated. Then the performance and behaviour of proposed semi-supervised SVMs are
studied under common experimental settings and their workability is established.