Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure

Dixon, Sarah J.; Brereton, Richard G.

doi:10.1016/j.chemolab.2008.07.010

Cited by 185 publications

(114 citation statements)

References 24 publications

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“…The main differences between the LDA and QDA are in the calculation of the classification score, in which QDA presents a more sophisticated approach accounting for different variance structures in the classes being analyzed. The LDA technique assumes that the classes have similar variance matrices, whereas QDA forms a separated variance model for each class (51). LDA and QDA classification scores are calculated as follows:…”

Section: Methodsmentioning

confidence: 99%

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Paraskevaidi

Morais

Lima

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

137

102

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The progressive aging of the world's population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer's disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein e4 genotype (APOE e4) information, increased to 86% when individuals carried one or two alleles of e4, and to 72% sensitivity and 77% specificity when individuals did not carry e4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson's disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.Alzheimer's disease | dementia with Lewy bodies | apolipoprotein E | differential diagnosis | spectroscopy

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Section: Methodsmentioning

confidence: 99%

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Paraskevaidi

Morais

Lima

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

137

102

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“…classes can be separated by lines in 2 dimensions, by planes in 3 dimensions and by hyperplanes in higher dimensional spaces). Potential drawbacks to LDA include weak performance when groups are strongly nested and a tendency towards overfitting (Dixon & Brereton 2009).…”

Section: Data Fish Collectionmentioning

confidence: 99%

Predicting trophic guild and diet overlap from functional traits: statistics, opportunities and limitations for marine ecology

Albouy¹,

Guilhaumon²,

Villéger³

et al. 2011

Mar. Ecol. Prog. Ser.

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Fish diets provide information that can be used to explore and model complex ecosystems, and infer resource partitioning among species. The exhaustive sampling of prey items captured by each species remains, however, a demanding task. Therefore, predicting diets from other variables, such as functional traits, may be a valuable method. Here, we attempted to predict trophic guild and diet overlap for 35 fish species using 13 ecomorphological traits related to feeding ecology. We compared linear discriminant analysis and random forest (RF) classifiers in their ability to predict trophic guild. We used generalized dissimilarity modelling to predict diet overlap from functional distances between species pairs. All models were evaluated using the same cross-validation procedure. We found that fish trophic guilds were accurately predicted by an RF classifier, even with a limited number of traits, when no more than 7 guilds were defined. Prediction was no longer accurate when finer trophic guilds were created (8 or more guilds), whatever the combination of traits. Furthermore, predicting the degree of diet dissimilarity between species pairs, based on their ecomorphological traits dissimilarities, was profoundly unreliable (at least 76% of unexplained variation). These results suggest that we can predict fish trophic guilds accurately from ecomorphological traits, but not diet overlap and resource partitioning because of inherent versatility in fish diets. More generally, our statistical framework may be applied to any kind of marine organism for which feeding strategies need to be determined from traits. KEY WORDS: Generalized dissimilarity modeling · Mediterranean · Fish · Non-linear model · Random forest · VersatilityResale or republication not permitted without written consent of the publisher Mar Ecol Prog Ser 436: 17-28, 2011 ing biotic indicators relevant to human impacts (SosaLopez et al. 2005). In addition, overlap in diet composition and resource partitioning between species is a key element of interspecific competition that can determine stable coexistence (Sala & Ballesteros 1997, Colloca et al. 2010. Determining the level of diet overlap among species is, therefore, also a major tool in predicting extirpations of species as a result of competitive interactions with invasive species (Karlson et al. 2007, Glen & Dickman 2008, Arismendi et al. 2009, Gregory & Macdonald 2009, Zeug et al. 2009).In practice, identifying the diet composition of species is a very time-consuming and demanding task with many potential biases. Indeed, a complete knowledge of prey items targeted by omnivorous species is unrealistic in prey-rich communities (Araújo et al. 2008). Diet composition is often assessed using stomach contents, which are influenced by many temporal (Lehikoinen 2005, Horppila 2009) and spatial factors (e.g. opportunistic behaviors; Link & Garrison 2002). Hence stomach content analysis is a time-consuming method that can only provide a fragmentary image of a species' diet.An alternative approach to ...

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“…In this study, the bootstrap [18][19][20][21][22] using 200 repetitions was used for determining the optimum number of PCs. Note that when performing the bootstrap, the bootstrap training set (which contains repetitions) is standardised, rather than the entire training or autopredictive dataset.…”

Section: Bootstrapmentioning

confidence: 99%

“…Whereas most methods for MSPC should be implementable online, procedures such as the bootstrap [18][19][20][21][22] and repeated division into training and test sets [11,[20][21][22] can usually be performed within seconds or minutes, making computationally intensive approaches for online monitoring feasible for real-time implementations.…”

mentioning

confidence: 99%

One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process

Kittiwachana

Ferreira

Lloyd

et al. 2010

Journal of Chemometrics

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In process monitoring, a representative out-of-control class of samples cannot be generated. Here, it is assumed that it is possible to obtain a representative subset of samples from a single 'in-control class' and one class classifiers namely Q and D statistics (respectively the residual distance to the disjoint PC model and the Mahalanobis distance to the centre of the QDA model in the projected PC space), as well as support vector domain description (SVDD) are applied to disjoint PC models of the normal operating conditions (NOC) region, to categorise whether the process is in-control or out-of-control. To define the NOC region, the cumulative relative standard deviation (CRSD) and a test of multivariate normality are described and used as joint criteria. These calculations were based on the application of window principal components analysis (WPCA) which can be used to define a NOC region. The D and Q statistics and SVDD models were calculated for the NOC region and percentage predictive ability (%PA), percentage model stability (%MS) and percentage correctly classified (%CC) obtained to determine the quality of models from 100 training/test set splits. Q, D and SVDD control charts were obtained, and 90% confidence limits set up based on multivariate normality (D and Q) or SVDD D value (which does not require assumptions of normality). We introduce a method for finding an optimal radial basis function for the SVDD model and two new indices of percentage classification index (%CI) and percentage predictive index (%PI) for non-NOC samples are also defined. The methods in this paper are exemplified by a continuous process studied over 105.11 h using online HPLC.

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Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure

Cited by 185 publications

References 24 publications

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Predicting trophic guild and diet overlap from functional traits: statistics, opportunities and limitations for marine ecology

One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process

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