A nature-inspired feature selection approach based on hypercomplex information (original) (raw)
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Mathematics, 2022
This survey is an effort to provide a research repository and a useful reference for researchers to guide them when planning to develop new Nature-inspired Algorithms tailored to solve Feature Selection problems (NIAs-FS). We identified and performed a thorough literature review in three main streams of research lines: Feature selection problem, optimization algorithms, particularly, meta-heuristic algorithms, and modifications applied to NIAs to tackle the FS problem. We provide a detailed overview of 156 different articles about NIAs modifications for tackling FS. We support our discussions by analytical views, visualized statistics, applied examples, open-source software systems, and discuss open issues related to FS and NIAs. Finally, the survey summarizes the main foundations of NIAs-FS with approximately 34 different operators investigated. The most popular operator is chaotic maps. Hybridization is the most widely used modification technique. There are three types of hybridiz...
2016
The dimensionality of the feature space when being high affects the classification accuracies and the computational complexity due to redundant, irrelevant and noisy features present in the dataset. Feature Selection extracts the more informative and distinctive features from any dataset to improve the classification accuracy. Nature Inspired Algorithms are famous meta-heuristic search algorithm used in solving combinatorial optimization problems. Previously, we have proposed FS algorithms based on ACO, ABC, EABC and by the convincing results produced by these algorithms we have proposed Firefly Algorithm(FA),Cuckoo Search(CA) ,Harmony Search(HAS) for feature selection procedure. This paper proposes a new method of feature selection by using FA to optimize the selection of features. Ten UCI datasets have been used for evaluating the proposed algorithm. Experimental results show that, FA-Feature Selection has resulted in optimal feature subset configuration and increased classificati...
Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data
Computers
Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various var...