Abstract: Feature selection and attribute reduction are crucial
problems, and widely used techniques in the field of machine
learning, data mining and pattern recognition to overcome the
well-known phenomenon of the Curse of Dimensionality. This paper
presents a feature selection method that efficiently carries out attribute
reduction, thereby selecting the most informative features of a dataset.
It consists of two components: 1) a measure for feature subset
evaluation, and 2) a search strategy. For the evaluation measure,
we have employed the fuzzy-rough dependency degree (FRFDD)
of the lower approximation-based fuzzy-rough feature selection
(L-FRFS) due to its effectiveness in feature selection. As for the
search strategy, a modified version of a binary shuffled frog leaping
algorithm is proposed (B-SFLA). The proposed feature selection
method is obtained by hybridizing the B-SFLA with the FRDD. Nine
classifiers have been employed to compare the proposed approach
with several existing methods over twenty two datasets, including
nine high dimensional and large ones, from the UCI repository.
The experimental results demonstrate that the B-SFLA approach
significantly outperforms other metaheuristic methods in terms of the
number of selected features and the classification accuracy.
Abstract: In this paper, performances of shuffled frog leaping
algorithm was investigated on the stealth laser dicing process. Effect
of problem on the performance of the algorithm was based on the
tolerance of meandering data. From the customer specification it
could be less than five microns with the target of zero microns.
Currently, the meandering levels are unsatisfactory when compared
to the customer specification. Firstly, the two-level factorial design
was applied to preliminarily study the statistically significant effects
of five process variables. In this study one influential process variable
is integer. From the experimental results, the new operating condition
from the algorithm was superior when compared to the current
manufacturing condition.
Abstract: Microarray technology is universally used in the study
of disease diagnosis using gene expression levels. The main
shortcoming of gene expression data is that it includes thousands of
genes and a small number of samples. Abundant methods and
techniques have been proposed for tumor classification using
microarray gene expression data. Feature or gene selection methods
can be used to mine the genes that directly involve in the
classification and to eliminate irrelevant genes. In this paper
statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR)
and F-Statistics are used to rank the genes. The ranked genes are used
for further classification. Particle Swarm Optimization (PSO)
algorithm and Shuffled Frog Leaping (SFL) algorithm are used to
find the significant genes from the top-m ranked genes. The Naïve
Bayes Classifier (NBC) is used to classify the samples based on the
significant genes. The proposed work is applied on Lung and Ovarian
datasets. The experimental results show that the proposed method
achieves 100% accuracy in all the three datasets and the results are
compared with previous works.
Abstract: Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.
Abstract: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).