Filter-Based Feature Selection Methods Using Hill Climbing Approach

Goswami, Saptarsi; Chakraborty, Sanjay; Guha, Priyanka; Tarafdar, Arunabha; Kedia, Aman

doi:10.1007/978-3-319-98566-4_10

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…At any position in state space, the search only continues in the direction that optimizes the cost of function in the hopes of eventually discovering the best answer [89]. The study utilized the random hill climbing algorithm successfully [90], [91], [92], [93].…”

Section: ) Random Hill Climbingmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering models. This research performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. The study aims to establish a structured formation related to popular methods such as filter, wrapper, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications are efficiently highlighted. The study focused on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which are also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.

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Section: ) Random Hill Climbingmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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“…Randomized search strategy starts by randomly selecting the features and then proceeds with two different search strategies. The first uses the classical sequential or bidirectional search, e.g., simulated annealing (Ezugwu et al 2017) and random hill-climbing (Goswami et al 2019). The second uses strategies that have no regular movements, e.g., genetic algorithm (GA) (Babatunde et al 2014), and Tabu search (Zhang & Sun, 2002) The second strategies can escape local optima in the search space, but they have a greater chance of producing incorrect results due to non-availability of mechanism to capture relationship between features.…”

Section: Global Optimal Feature Selectionmentioning

confidence: 99%

A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Kyaw

Limsiroratana

Sattayaraksa

2022

International Journal of Applied Metaheuristic Computing

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Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.

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“…Findings in the literature reveal that FS provides several benefits in modeling and analysis. With fewer features than the original configuration, models are much easier to interpret [24]. Similarly, with fewer features, the models become less expensive to train due to less memory and computation cost [25].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Feature Selection in Binary Machine Learning Classification: A Novel Application of the Evaluation Based on Distance from Average Solution (EDAS) Method

Abellana

Roxas

Lao

et al. 2022

Mathematical Problems in Engineering

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Combining filters in an ensemble to improve feature selection performance is a growing field in the literature. Current techniques, however, are focused on approaches that suffer from drawbacks such as sensitivity to skewed distribution, among others. To address this gap, this paper investigates the applicability of multiple criteria decision-making in ensemble feature selection. This paper adopts the Evaluation based on Distance from Average Solution (EDAS) method due to its many familiar elements to the feature selection community. An experiment was performed on six datasets and a control group. The paper uses the six datasets as levels of the blocking factor. A negative control group (i.e., no feature selection) was adopted to compare with the proposed algorithm. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. The findings in this study would contribute to the literature in several ways. First, the paper is one of the few works to demonstrate how MCDM can be used in feature selection with promising results. Second, this paper is one of the few works to demonstrate the significance of including datasets as levels of a blocking factor when performing significance testing. Finally, this paper is the first to demonstrate the applicability of EDAS as an ensemble FS algorithm. As such, the findings in this paper could spark the cross-fertilization of feature selection and MCDM.

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Filter-Based Feature Selection Methods Using Hill Climbing Approach

Cited by 14 publications

References 23 publications

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Ensemble Feature Selection in Binary Machine Learning Classification: A Novel Application of the Evaluation Based on Distance from Average Solution (EDAS) Method

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