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Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

机译：软件独立建模类类比监督模式识别的合理选择吗？

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摘要

A thorough survey of classification data sets and a rigorous comparison of classification methods clearly show the unambiguous superiority of other techniques over soft independent modeling of class analogies (SIMCA) in the case of classification -which is a frequent area of usage for SIMCA, even though it is a class modeling(one class or disjoint class modeling technique). Two non-parametric methods, sum of ranking differences (SRD) and the generalized pairwise correlation method (GPCM) have been used to rank and group the classifiers obtained from six case studies. Both techniques need a supervisor (a reference) and their results support and validate each other, despite being based on entirely different principles and calculation procedures. To eliminate the effect of the chosen reference, comparisons with one variable (classifier) at a time were calculated and presented as heatmaps. Six case studies show unambiguously that SIMCA is inferior to other classification techniques such as linear and quadratic discriminant analyses, multivariate range modeling, etc. This analysis is similar to meta-analyses frequently applied in medical science nowadays; with the notable difference that we did not (and should not) make any distributional assumptions. A well-founded conclusion can be drawn, as we could not find any circumstances when SIMCA is superior to concurrent techniques. Hence, the question in the title is self-explanatory.
机译：对分类数据集的彻底调查以及分类方法的严格比较清楚地显示了在分类的情况下对类模拟（SIMCA）的软独立建模的其他技术的明确优势 - 即使是SIMCA的频繁使用领域它是一个类建模（一个类或脱编类建模技术）。两个非参数方法，排名差异（SRD）和广义的成对相关方法（GPCM）已经用于等级和分组从六个案例研究获得的分类器。尽管基于完全不同的原则和计算程序，但这两种技术都需要一个主管（参考）及其结果支持并验证彼此。为了消除所选择的参考的效果，计算和呈现与一个变量（分类器）的比较被计算并呈现为Heatmaps。六个案例研究明确地显示了SIMCA差别不如其他分类技术，例如线性和二次判别分析，多变量范围建模等。该分析类似于现在在医学科学中经常应用的Meta-Analys;随着我们没有（不应该）的显着差异，产生任何分配假设。可以绘制一个良好的结论，因为我们在SIMCA优于并发技术时，我们无法找到任何情况。因此，标题中的问题是不言自明的。

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  • 来源
    《RSC Advances》 |2018年第1期|共12页
  • 作者

    Racz Anita; Gere Attila; Bajusz David; Heberger Karoly;

  • 作者单位

    Hungarian Acad Sci Res Ctr Nat Sci Plasma Chem Res Grp Magyar Tudosok Krt 2 H-1117 Budapest Hungary;

    Szent Istvan Univ Fac Food Sci Sensory Lab Villanyi Ut 29-43 H-1118 Budapest Hungary;

    Hungarian Acad Sci Res Ctr Nat Sci Med Chem Res Grp Magyar Tudosok Krt 2 H-1117 Budapest Hungary;

    Hungarian Acad Sci Res Ctr Nat Sci Plasma Chem Res Grp Magyar Tudosok Krt 2 H-1117 Budapest Hungary;

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  • 正文语种 eng
  • 中图分类 化学;
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