Thirugnanasambandam Kalaipriyan, Murugan Jayalakshmi, Ramalingam Rajakumar, Rashid Mamoon, Raghav R S, Kim Tai-Hoon, Sampedro Gabriel Avelino, Abisado Mideth
Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India.
PeerJ Comput Sci. 2024 Jan 29;10:e1816. doi: 10.7717/peerj-cs.1816. eCollection 2024.
Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal.
In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features.
The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.
特征选择是数据挖掘和机器学习方法中的一个重要过程，它通过确定可用特征中哪些特征最适合分类或知识表示。然而，具有挑战性的任务是从给定的特征集中找到一个选定的元素子集，以便从原始数据中表示或提取知识。所选特征的数量应适当限制且足够多，以防止结果偏离准确性。在计算时间成本方面，特征选择至关重要。本研究提出了一种特征选择模型，以解决多模态的特征选择问题。
在这项工作中，一种受杜鹃鸟行为启发的新型优化算法是二进制强化杜鹃搜索算法（BRCSA）。此外，我们将基于BRCSA提出的分类方法应用于多模态特征选择。所提出的方法旨在从多个模态中选择最相关的特征，以提高模型的分类性能。BRCSA算法用于优化特征选择过程，并采用二进制编码方案来表示所选特征。
在几个基准数据集上进行了实验，并将结果与其他现有最先进的特征选择方法进行比较，以评估所提出方法的有效性。实验结果表明，基于BRCSA提出的方法在分类准确性方面优于其他方法，表明其在实际应用中的潜在适用性。具体而言，在分类准确率（平均值）方面，所提出的算法优于现有方法，如DGUFS，优势为32%；优于MBOICO，优势为24%；优于MBOLF，优势为29%；优于WOASAT，优势为22%；优于BGSA，优势为28%；优于HGSA，优势为39%；优于FS - BGSK，优势为37%；优于FS - pBGSK，优势为42%；优于BSSA，优势为40%。
