Comments on "On the mean accuracy of statistical pattern recognizers" by Hughes, G. F.

Abend, Kenneth; Harley, Thomas J.; Chandrasekaran, B.; Hughes, Gary W.

doi:10.1109/tit.1969.1054314

Cited by 49 publications

(11 citation statements)

References 5 publications

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“…Hughes noted, "If insufficient sample data are available to estimate the pattern probabilities accurately, then a Bayes recognizer is not necessarily optimal". 53,57 Various researchers correctly criticized Hughes' work by pointing out that the paradoxial peaking phenomenon observed therein was not real and was due to the estimate of unknown cell probabilities from the data. [57][58][59][60] In other words, the peaking phenomenon observed by Hughes was essentially within a frequentist framework, not a Bayesian.…”

Section: Chandrasekaran and Jain Pointed Outmentioning

confidence: 99%

High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries

Zollanvari

2015

Cancer Inform

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High-dimensional data generally refer to data in which the number of variables is larger than the sample size. Analyzing such datasets poses great challenges for classical statistical learning because the finite-sample performance of methods developed within classical statistical learning does not live up to classical asymptotic premises in which the sample size unboundedly grows for a fixed dimensionality of observations. Much work has been done in developing mathematical–statistical techniques for analyzing high-dimensional data. Despite remarkable progress in this field, many practitioners still utilize classical methods for analyzing such datasets. This state of affairs can be attributed, in part, to a lack of knowledge and, in part, to the ready-to-use computational and statistical software packages that are well developed for classical techniques. Moreover, many scientists working in a specific field of high-dimensional statistical learning are either not aware of other existing machineries in the field or are not willing to try them out. The primary goal in this work is to bring together various machineries of high-dimensional analysis, give an overview of the important results, and present the operating conditions upon which they are grounded. When appropriate, readers are referred to relevant review articles for more information on a specific subject.

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Section: Chandrasekaran and Jain Pointed Outmentioning

confidence: 99%

High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries

Zollanvari

2015

Cancer Inform

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“…This relatively small value highlights the conservative character of Hughes' distribution-free approach; for example, in practice, where one deals with a fixed distribution of the data, the optimal number of features would typically be larger than the ones observed in Fig. ( 8 ), so that sample size recommendations based on this analysis tend to be pessimistic --- a fact that was pointed out in [ 37 ]. Nevertheless, the qualitative behavior of the analysis is entirely correct.…”

Section: Small-sample Performance Of Discrete Classificationmentioning

confidence: 99%

Classification and Error Estimation for Discrete Data

Braga-Neto

2009

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Discrete classification is common in Genomic Signal Processing applications, in particular in classification of discretized gene expression data, and in discrete gene expression prediction and the inference of boolean genomic regulatory networks. Once a discrete classifier is obtained from sample data, its performance must be evaluated through its classification error. In practice, error estimation methods must then be employed to obtain reliable estimates of the classification error based on the available data. Both classifier design and error estimation are complicated, in the case of Genomics, by the prevalence of small-sample data sets in such applications. This paper presents a broad review of the methodology of classification and error estimation for discrete data, in the context of Genomics, focusing on the study of performance in small sample scenarios, as well as asymptotic behavior.

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“…Another major challenge for HS image processing is the large amount of data. An imager can capture tens or hundreds of frames, which can lead to the Hughes phenomenon, whereby the accuracy gradually increases as the number of dimensions increases, but decreases after a certain number of dimensions is reached [ 16 ], as well as producing redundancy among the samples [ 17 ]. These challenges can be avoided computationally, for instance, by employing common feature extraction methods.…”

Section: Introductionmentioning

confidence: 99%

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Lindholm

Raita-Hakola

Annala

et al. 2022

JCM

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Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

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Comments on "On the mean accuracy of statistical pattern recognizers" by Hughes, G. F.

Cited by 49 publications

References 5 publications

High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries

High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries

Classification and Error Estimation for Discrete Data

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

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