A Review of Swarm Intelligence-Based Feature Selection Methods and Its Application

Janaki, M.; Geethalakshmi, S. N.

doi:10.1007/978-981-19-3590-9_34

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Feature ranking algorithms are used to optimize feature selection in this study. The feature ranking algorithm based on specific criteria is to compute an importance score for each feature and rank the features based on this score [34]. There are some commonly used ranking algorithms, including feature weights [35], model-based feature importance [36][37][38], permutation feature importance [36] and SHAP-based feature importance [39].…”

Section: Ranking Algorithm and Feature Selectionmentioning

confidence: 99%

Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information

Wu,

Feng,

et al. 2024

Mathematics

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Keyword pools are used as search queries to collect web texts, largely determining the size and coverage of the samples and provide a data base for subsequent text mining. However, how to generate a refined keyword pool with high similarity and some expandability is a challenge. Currently, keyword pools for search queries aimed at collecting web texts either lack an objective generation method and evaluation system, or have a low utilization rate of sample semantic information. Therefore, this paper proposed a keyword generation framework that integrates sample and semantic information to construct a complete and objective keyword pool generation and evaluation system. The framework includes a data phase and a modeling phase, and its core is in the modeling phase, where both feature ranking and model performance are considered. A regression model about a topic vector and word vectors is constructed for the first time based on word embedding, and keyword pools are generated from the perspective of model performance. In addition, two keyword generation methods, Recursive Feature Introduction (RFI) and Recursive Feature Introduction and Elimination (RFIE), are also proposed in this paper. Different feature ranking algorithms, keyword generation methods and regression models are compared in the experiments. The results show that: (1) When using RFI to generate keywords, the regression model using ranked features has better prediction performance than the baseline model, and the number of generated keywords is refiner, and the prediction performance of the regression model using tree-based ranked features is significantly better than that of the one using SHAP-based ranked features. (2) The prediction performance of the regression model using RFI with tree-based ranked features is significantly better than that using Recursive Feature Elimination (RFE) with tree-based one. (3) All four regression models using RFI/RFE with SHAP- based/tree-based ranked features have significantly higher average similarity scores and cumulative advantages than the baseline model (the model using RFI with unranked features). (4) Light Gradient Boosting Machine (LGBM) using RFI with SHAP-based ranked features has significantly better prediction performance, higher average similarity scores, and cumulative advantages. In conclusion, our framework can generate a keyword pool that is more similar to the topic, and more refined and expandable, which provides certain research ideas for expanding the research sample size while ensuring the coverage of topics in web text collecting.

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Section: Ranking Algorithm and Feature Selectionmentioning

confidence: 99%

Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information

Wu,

Feng,

et al. 2024

Mathematics

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“…The study of the behavior of social organisms as a swarm in and outside their colonies led to Swarm Intelligence (SI) (Eberhart, Shi, & Kennedy, 2001;Janaki & Geethalakshmi, 2022;Selvaraj & Choi, 2020). SI is a discipline in computer science that mimics the intelligence displayed by social organisms (Kaswan, Dhatterwal, & Kumar, 2023;Schumann, 2020).…”

Section: Introductionmentioning

confidence: 99%

Foraging Bee Optimization Algorithm

Fasina,

Sawyerr,

Alkassim

2023

IJIEM

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Honey bee colonies depend on pollen and nectar from flowers for their feed. The act of searching for this flowers by the bees is called foraging. The foraging behaviour of bees depends on the profitability of nectar and pollen sources as well as the needs of the colony. This behaviour is modeled into an algorithm called Foraging Bee Optimization Algorithm (FBA). After initialization, the algorithm loops through three phases based on bees’ nature foraging behaviour called the 3W: Waggle, Work, and Withdraw. A large number of flowers are initialized randomly in the problem space. During the waggle phase, bees are recruited to patch with profitable nectar sources. In the work phase, new flowers are discovered and memorized by bees. In the withdraw phase bees eliminate unprofitable flowers and recalibrate for recruitment. The proposed FBA is tested on three unimodal and twelve multimodal benchmark. The result is compared with two state-of-the-art natured-inspired optimization algorithm.

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Spatial evolution of water transportation industry based on multi-source data: understanding the structural consolidation and integration demand in coastal cities

Wang

Zhang

Yan³

2023

Human and Ecological Risk Assessment: An International Journal

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A Review of Swarm Intelligence-Based Feature Selection Methods and Its Application

Cited by 3 publications

References 21 publications

Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information

Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information

Foraging Bee Optimization Algorithm

Spatial evolution of water transportation industry based on multi-source data: understanding the structural consolidation and integration demand in coastal cities

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