交叉验证的向量广义加性模型多标签分类

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Amri Muhaimin, Wahyu Wibowo, Prismahardi Aji Riyantoko
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引用次数: 0

摘要

多标签分类是机器学习中一个独特的挑战,设计用于两个目标,每个目标包含一个或多个类。这个问题可以通过几种方法来解决,包括单独或同时对目标进行分类。然而,大多数模型不能同时对目标进行分类,这在建模规则中是不希望发生的。本研究提出了一种新的解决方案,即使用交叉验证的向量广义加性模型(VGAMCV)来解决这些问题。该方法利用向量广义加性模型(VGAM)作为基础模型,该模型是一种结合参数和非参数成分的半参数模型。并应用交叉验证对参数进行调整,以优化方法的性能。此外,将VGAMCV的方法与多标签分类中常用的基于树的模型Random Forest进行比较,并基于14个指标分数评估其有效性。结果显示,平均准确率为0.703,曲线下面积(AUC)为0.601,但这些改善没有统计学意义。同时,该方法为多标签分类任务提供了一种可行的替代方法,它的引入有助于扩展可用于此目的的方法库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation
Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously.However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This studywas conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) toaddress these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametricmodel combining both parametric and non-parametric components as the underlying base model. Cross-validation was also appliedto tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with atree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metricscores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, butthese improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classificationtasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose.
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来源期刊
Journal of Information and Communication Technology-Malaysia
Journal of Information and Communication Technology-Malaysia COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.00
自引率
25.00%
发文量
21
审稿时长
12 weeks
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