One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case
  Study on Time Series Classification
  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.
