使用二元粒子群优化和合成特征的特征选择模型

AI Pub Date : 2024-07-25 DOI:10.3390/ai5030060
S. Ojo, J. Adisa, P. Owolawi, Chunling Tu
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引用次数: 0

摘要

识别看似随机和随机性质的数据之间的模式并推断其非线性关系,是机器学习模型的强项之一。在给定一组特征的情况下,如何区分有用特征和看似无用的特征,进而提取出能对高度随机的数据做出最佳预测的特征子集,仍然是一个有待解决的问题。本研究通过生成合成特征并应用基于长短期记忆模型的二元粒子群优化技术,提出了一种特征选择模型。该研究分析了数据之间的相关性,并将苹果股票市场数据作为使用案例。从与标签相关性弱/低的特征中创建了合成特征,并分析了描述特征的合成特征如何增强模型的预测能力。结果表明,在应用特征选择之前扩展数据集以包含合成特征,与不添加合成特征的情况相比,目标函数得到了更好的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model for Feature Selection with Binary Particle Swarm Optimisation and Synthetic Features
Recognising patterns and inferring nonlinearities between data that are seemingly random and stochastic in nature is one of the strong suites of machine learning models. Given a set of features, the ability to distinguish between useful features and seemingly useless features, and thereafter extract a subset of features that will result in the best prediction on data that are highly stochastic, remains an open issue. This study presents a model for feature selection by generating synthetic features and applying Binary Particle Swarm Optimisation with a Long Short-Term Memory-based model. The study analyses the correlation between data and makes use of Apple stock market data as a use case. Synthetic features are created from features that have weak/low correlation to the label and analysed how synthetic features that are descriptive of features can enhance the model’s predictive capability. The results obtained show that by expanding the dataset to contain synthetic features before applying feature selection, the objective function was better optimised as compared to when no synthetic features were added.
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AI
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