Abstract
Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.
Highlights
In recent times, there has been a growing interest in developing and utilizing metaheuristic population-based optimization algorithms to solve combinatorial optimization problems
To intuitively verify the efficiency of this Improved BBO (IBBO) algorithm and its optimization performance, the results have been compared with eight other optimization algorithms and analysed in detail
Hybrid algorithms are highly efficient from the basic versions, as they benefit from all the advantages of their basic algorithms
Summary
There has been a growing interest in developing and utilizing metaheuristic population-based optimization algorithms to solve combinatorial optimization problems This is mainly due to the simplicity, inexpensive computational cost, gradient-free mechanism, and flexibility of them. The problem of optimization grows bigger when handling large volume datasets, as there would be a large feature space with wide number of classes These datasets cause problems to machine learning and make the task of classification difficult to solve. As a result, choosing the discriminative features demand an extreme importance towards the construction of efficient classifiers with high predictive accuracy To overcome this problem, one efficient way is to select a small subset of information-rich features from these large volume datasets (using an optimization algorithm) that best describes the target concept. This technique is known as feature selection (FS) and it helps in solving the data overfitting problem by getting rid of noisy features, reducing the computational load, and increasing the overall classification performance of the learning models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
