Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by FS for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive
-Hill Climbing (A
HC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at:
https://github.com/khalid0007/Metaheuristic-Algorithms
.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.