Author:
Muhammad Marwan Muhammad Fuad
Affiliation:
The University of Tromsø - The Arctic University of Norway, Norway
Keyword(s):
Bio-inspired Optimization, Differential Evolution, Overfitting, Time Series Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
For the last few decades, optimization has been developing at a fast rate. Bio-inspired optimization
algorithms are metaheuristics inspired by nature. These algorithms have been applied to solve different
problems in engineering, economics, and other domains. Bio-inspired algorithms have also been applied in
different branches of information technology such as networking and software engineering. Time series data
mining is a field of information technology that has its share of these applications too. In previous works we
showed how bio-inspired algorithms such as the genetic algorithms and differential evolution can be used to
find the locations of the breakpoints used in the symbolic aggregate approximation of time series
representation, and in another work we showed how we can utilize the particle swarm optimization, one of
the famous bio-inspired algorithms, to set weights to the different segments in the symbolic aggregate
approximation representation. In this paper we present,
in two different approaches, a new meta
optimization process that produces optimal locations of the breakpoints in addition to optimal weights of the
segments. The experiments of time series classification task that we conducted show an interesting example
of how the overfitting phenomenon, a frequently encountered problem in data mining which happens when
the model overfits the training set, can interfere in the optimization process and hide the superior
performance of an optimization algorithm.
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