A principal label space transformation and ridge regression-based hybrid gorilla troops optimization and jellyfish search algorithm for multi-label classification

Seyed Hossein Seyed Ebrahimi, Kambiz Majidzadeh, Farhad Soleimanian Gharehchopogh
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Abstract

Classification as an essential part of Machine Learning and Data Mining has significant roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection tools and the unceasing efforts of researchers, new datasets with huge dimensions are obtained so that each data sample has multiple labels. This kind of classification is called Multi-Class Classification (MLC) and demands new techniques to predict the set of labels for a data instance. To date, a variety of methods have been proposed to solve MLC problems. However, new high-dimensional datasets with challenging patterns are being developed, making it necessary for new research to be conducted to develop more efficient methods. This paper presents a novel framework named QLHA to solve MLC problems more efficiently. In the QLHA, the Principal Label Space Transformation (PLST) and Ridge Regression (RR) are recruited to predict the labels of data. Next, an effective objective function is introduced. Also, a hybrid metaheuristic algorithm called QGTOJS is provided to optimize objective value and enhance the predicted labels by selecting the most relevant features. In the QGTOJS, the Gorilla Troops Optimization (GTO) and Jellyfish Search algorithm (JS) are binarized and hybridized through a modified variant of the Q-learning algorithm. Besides, an adjusted Hill Climbing strategy is adopted to balance the exploration and exploitation and improve local optima departure. Likewise, a local search mechanism is provided to enhance searchability as much as possible. Eventually, the QLHA is applied to ten multi-label datasets and the obtained results are compared with heuristic and metaheuristic-based MLC methods numerically and visually. The experimental results disclosed the effectiveness of the contributions and superiority of the QLHA over competitors.

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基于主标签空间转换和脊回归的多标签分类混合猩猩部队优化和水母搜索算法
分类作为机器学习和数据挖掘的重要组成部分,在工程、医学、农业、军事等领域发挥着重要作用。随着数据收集工具的发展和研究人员的不懈努力,人们获得了具有巨大维度的新数据集,因此每个数据样本都有多个标签。这种分类被称为多类分类(MLC),需要新的技术来预测数据实例的标签集。迄今为止,已经提出了多种方法来解决 MLC 问题。然而,具有挑战性模式的新高维数据集正在开发中,因此有必要开展新的研究,以开发更有效的方法。本文提出了一种名为 QLHA 的新型框架,用于更高效地解决 MLC 问题。在 QLHA 中,采用了主标签空间变换(PLST)和岭回归(RR)来预测数据的标签。接着,引入了一个有效的目标函数。此外,还提供了一种名为 QGTOJS 的混合元启发式算法,以优化目标值,并通过选择最相关的特征来增强预测标签。在 QGTOJS 中,猩猩部队优化算法(GTO)和水母搜索算法(JS)被二值化,并通过 Q-learning 算法的修改变体进行混合。此外,还采用了调整后的爬坡策略,以平衡探索和利用,改善局部最优出发。同样,还提供了一种局部搜索机制,以尽可能提高可搜索性。最后,QLHA 被应用于十个多标签数据集,并与基于启发式和元启发式的 MLC 方法进行了数值和视觉比较。实验结果表明了 QLHA 所做贡献的有效性以及优于竞争对手的优势。
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