A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen, Hongwei; Zhou, Xun; Shi, Dewei

doi:10.1007/s44196-022-00094-5

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…The KNN classifier is also employed to evaluate candidate features [77]. In [78], the chaotic antlion feature selection algorithm (CAFSA) was introduced to enhance the ALO algorithm performance while solving the FS problem for textual data. In CAFSA, the chaos map and opposite learning concept were utilized to improve global and local search capabilities, respectively.…”

Section: Reviewing the Applications Of Mas In Fsmentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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Recent technological advances in medical diagnosis have led to the generation of high-dimensional datasets. The presence of redundant and irrelevant features in these datasets can have adverse effects on the performance of machine learning (ML) methods and reduce the accuracy of their results. Therefore, feature selection (FS), i.e., a popular preprocessing method in ML, is used to select the optimal subsets of features to improve the accuracy of ML methods. This performance enhancement is more crucial while addressing high-dimensional medical issues. Since FS is a multiobjective binary optimization problem, it is necessary to develop efficient FS algorithms. Although metaheuristic algorithms (MAs) have been widely used for FS in medicine, they face different challenges in most applications, e.g., a lack of sufficient effectiveness and scalability to select the most effective features in small and large medical datasets. The cat and mouse-based optimizer (CMBO) is a novel MA based on the natural competitive behavior of cats and mice. Despite its acceptable performance in a variety of problems, the CMBO faces various challenges such as limited exploitation abilities, an unbalanced search mechanism, and high fluctuation in solutions to complex problems, e.g., FS. This paper proposes a modified and binary version of the CMBO called the BMCMBO to enhance the performance in selecting effective features from medical datasets. The BMCMBO involves significant modifications to the method of updating the positions of search agents, the method of selecting mice, the effect of the positional information of the most optimal member of the population, and the addition of the adaptive step size. These modifications are meant to improve the exploitation abilities, boost the accuracy of the solutions, and balance the search process when dealing with the FS problem in medical datasets. The performance of the proposed algorithm on 12 real medical datasets was compared with the performance of the most effective MA and CMBO variants. The statistical results demonstrated that BMCMBO was more effective than other evaluated methods. In addition, the BMCMBO algorithm was employed to select features and diagnose COVID-19 in a real case study. The proposed algorithm identified healthy and infected COVID-19 correctly samples with an accuracy of 98.4\%, demonstrating its superiority.

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Section: Reviewing the Applications Of Mas In Fsmentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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“…A nature-enthused algorithm that follows the hunting behavior of antlion larvae is called the Ant Lion Optimization (ALO) [32] method. An antlion digs a coneshaped pit and conceals its larvae below the cone-shaped pit's bottom to trap the ant.…”

Section: ) Bsp-basedalo For Lstm Optimizationmentioning

confidence: 99%

An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling

C.,

P. M.

2023

JAIT

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Incessant COVID-19 pandemic negatively impacts nations throughout the globe. It is necessary to determine how people react to public health interventions and understand their concerns. Twitter is a social media platform that has emerged as a tool for disseminating information, debating concepts, and reviewing or commenting on global issues. This study applies Explainable Artificial Intelligence (XAI) methods, like Cosine Similarity and Polynomial Kernel-centered Fuzzy C-Means (CSPK-FCM) centered topic modeling and Fuzzy Logic with Improved Long Short-Term Memory (FL-ILSTM) centered Sentiment Analysis to COVID-19 data on Twitter. The proposed model has five major steps: preprocessing, feature extraction, term weighting, topic modeling (clustering), and classification. Twitter comments relating to the COVID-19 pandemic are initially collected from publicly accessible websites. The collected data are then preprocessed to remove irrelevant information, namely, noises. The Feature Extraction phase is then performed by extracting emoticon and non-emoticon features. The extracted feature dataset is scored: the Term Frequency Inverse Document Frequency-Chi-Square (TFIDF-CHI) method is utilized for non-emoticon, and the score for the emoticon is assigned based on a few criteria. For Topic modeling, the TFIDF-CHI scores are provided to the CSPK-FCM clustering algorithm, which groups the most frequently discussed topics throughout COVID-19. FL-ILSTM executes the Sentiment analysis of clustered topics and emoticon features. It has extraordinary performance when compared to other methodologies.

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Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems

Zhong,

Zhang,

2024

Knowl Inf Syst

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A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Cited by 5 publications

References 48 publications

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling

Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems

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