الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
Classi cation problems often have a large number of features, but not all of them are
useful for classi cation. Most of the real-valued datasets are high dimensional datasets
which contain noisy (irrelevant and redundant) features. Such datasets may reduce the
classi cation accuracy. Feature selection (FS) is the process of selecting a subset of
relevant features, which can decrease the dimensionality, shorten the running time and
improve the classi cation accuracy. Consequently, using FS helps to improve classi cation
systems.
There are three types of FS approaches, i.e. wrapper, lter and embedded approaches.
Their main di erence is that wrappers use a classi cation algorithm to evaluate the
goodness of the features during the FS process while lters are independent of any classi
cation algorithm. The embedded approach relaxed the cost of the wrapper approach
by incorporating the FS during the training step of the classi er. FS is a dicult task
because of feature interactions and the large search space.
Existing FS methods su er from di erent problems, such as stagnation in local optima
and high computational cost. Bio-inspired Optimization Algorithms (BIAs) are wellknown
global search algorithms, computationally less expensive and can converge faster
than other methods. BIAs have been successfully applied to many areas, but their
potentials for FS has not been fully investigated. Whale Optimization Algorithm (WOA)
and Salp Swarm Algorithm (SSA) are BIAs that are computationally less expensive and
can converge faster than other methods. WOA and SSA have been successfully applied
to many areas, but their potential for FS has not been fully investigated.
The overall goal of this thesis is to investigate and improve the capability of BIAs for
FS to select a smaller number of features and achieve similar or better classi cation
performance than using all features. To overcome these limitations, this thesis suggests
a new four BIAs wrapper FS models namely: Improved Whale Optimization Algorithm
(IWOA) , Improved Salp Swarm Algorithm (ISSA), Chaotic Salp Swarm Algorithm
(CSSA) and Fuzzy Mutual Information Binary Salp Swarm Algorithm (FMIBSSA).
Based on benchmark datasets, the proposed models were evaluated according to the
number of selected feature, classi cation accuracy, tness values and run-time. The
experimental results con rm that our proposed models achieve better results, improve
the classi cation performance, reduce the number of features and decrease computational
time.