监督学习算法在WEKA中拖鞋质量判定中的应用

IF 0.4 Q4 MULTIDISCIPLINARY SCIENCES
Jennilyn C. Mina
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引用次数: 0

摘要

本研究的目的是评估各种回归算法在预测拖鞋质量方面的有效性。所选择的回归算法在怀卡托知识分析环境中实现。通过分析相关系数对其性能进行评估,从而深入了解其预测能力。值得注意的是,随机森林算法显示出最高的预测能力,其相关系数(r=0.76)令人印象深刻,在分析中超过了其他模型。继Random Forest之后,k近邻算法获得了可观的相关系数(r=0.65),其次是决策树(r=0.53)、线性回归(r=0.51)和多层感知器(r=0.51)。相比之下,支持向量机的相关系数明显较低(r=0.51),表明其预测性能相对较弱。此外,本研究还揭示了两个变量,“易洗”和“耐水”,它们分别显示出与回归模型的预测性能相关的显著相关性(r=0.49)和(r=-0.35)。然而,其他变量之间没有显著的相关性。根据这些发现,未来的研究可能会探索其他预测模型,以进一步评估和比较它们与本研究结果的表现,为不断提高拖鞋质量预测方法做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of supervised learning algorithm to determine the quality of slippers in WEKA
This study is driven by the objective of evaluating the effectiveness of various regression algorithms in the prediction of slipper quality. The selected regression algorithms were implemented within the Waikato Environment for Knowledge Analysis. The assessment of their performance was conducted through the analysis of correlation coefficients, providing insights into their predictive capabilities. Notably, the Random Forest algorithm demonstrated the highest predictive power with an impressive correlation coefficient (r=0.76), surpassing other models in the analysis. Following Random Forest, the k-nearest neighbor algorithm achieved a substantial correlation coefficient of (r=0.65), followed by the Decision Tree (r=0.53), Linear regression (r=0.51), and the Multi-layer perceptron (r=0.51). In contrast, the Support Vector Machine showed a notably lower correlation coefficient (r=0.51), indicating its comparatively weaker predictive performance. Furthermore, this study uncovered two variables, "Easy to Wash" and "Water Resistance," which displayed significant correlations of (r=0.49) and (r=-0.35), respectively, in relation to the predictive performance of the regression model. However, no significant correlation was observed for other variables. In light of these findings, future research endeavors may explore alternative predictive models to further assess and compare their performance against the outcomes presented in this study, contributing to the ongoing enhancement of slipper quality prediction methodologies.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
234
审稿时长
8 weeks
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