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Issue 1, 2018, Issue in Progress
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Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

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Abstract

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.

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

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Publication details

The article was received on 11 Aug 2017, accepted on 12 Dec 2017 and first published on 20 Dec 2017


Article type: Paper
DOI: 10.1039/C7RA08901E
Citation: RSC Adv., 2018,8, 10-21
  • Open access: Creative Commons BY-NC license
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    Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

    A. Rácz, A. Gere, D. Bajusz and K. Héberger, RSC Adv., 2018, 8, 10
    DOI: 10.1039/C7RA08901E

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