Modern methods for old data: An overview of some robust methods for outliers detection with applications in osteology

Santos, Frédéric

doi:10.1016/j.jasrep.2020.102423

Cited by 7 publications

(6 citation statements)

References 51 publications

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“…Voor dit project worden outliers gedefinieerd als iedereen wiens waarden groter of kleiner zijn dan het gemiddelde met twee maal de standaard deviatie, zoals ook de norm is in de archeologische wetenschappen (Santos 2020). Dit wordt consistent zo toegepast voor alles sites afzonderlijk en voor de complete site-overspannende dataset.…”

Section: Outliers En Afwijkende Resultaten Bij Individuen Met Pathologieunclassified

Wat schaft het bot? Een vergelijkend onderzoek naar eetgewoonten en indicatoren voor sociale gelaagdheid van stedelijke en landelijke populaties in Zuid-Oost-Vlaanderen, aan de hand van stabiel isotopenonderzoek op menselijk botmateriaal

Palmer,

Massagé,

Cherretté

2024

Syntar

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Section: Outliers En Afwijkende Resultaten Bij Individuen Met Pathologieunclassified

Wat schaft het bot? Een vergelijkend onderzoek naar eetgewoonten en indicatoren voor sociale gelaagdheid van stedelijke en landelijke populaties in Zuid-Oost-Vlaanderen, aan de hand van stabiel isotopenonderzoek op menselijk botmateriaal

Palmer,

Massagé,

Cherretté

2024

Syntar

View full text Add to dashboard Cite

“…In addition, multivariate outliers must be checked before conducting EFA [56]. A robust Mahalanobis distance (significant at α = 0.01) was used [57]. Watkins [58] argued that in the presence of multivariate outliers, the use of polychoric correlations might be more appropriate.…”

Section: Exploratory Factor Analysismentioning

confidence: 99%

“…Some clustering methods are sensitive to outliers [68]. To determine the appropriate clustering method, the data were checked for multivariate outliers using a robust Mahalanobis distance with a significance level of p < 0.01 [57].…”

Section: Cluster Analysismentioning

confidence: 99%

Benefit Segmentation of Tourists to Geosites and Its Implications for Sustainable Development of Geotourism in the Southern Lake Tana Region, Ethiopia

et al. 2022

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Geotourism is a sustainable type of tourism that focuses on the geological and geomorphological heritages of an area, and the associated cultural and biodiversity features. Though the popularity of geotourism is rapidly growing, research on the demand side, particularly on segmenting tourists to geosites and understanding their profiles, is limited. This obviously makes the designing of effective tourism policies that aim at developing geotourism sustainably very difficult. Hence, the main objectives of this study were to segment and profile tourists to geosites based on the benefits sought, and to show its implications for sustainable development of geotourism. With a survey of 415 tourists, this study clustered tourists to geosites in the southern Lake Tana region in Ethiopia based on the benefits sought. A factor–cluster method was applied to segment the tourists. The study identified four distinct segments: Activity–Nature Lovers, Culture Lovers, Nature–Culture Lovers, and Want-It-Alls. These segments differed in their demographic, trip, and behavioral characteristics. The findings implied that for sustainable development, destination managers and marketers need to customize their geotourism product development and marketing strategies based on the needs and characteristics of each market segment.

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“…The extreme value identifications were carried out via standardization at the value of two and three standard deviations from the mean. Various predictive methods have been used for detection of outliers in the dataset [23][24][25][26][27]. The authors remarked that neural networks and linear models are sensitive to noise points, whereas decision trees are robust to outliers.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

Balancing Complex Signals for Robust Predictive Modeling

Aman

Rauf

Ali

et al. 2021

Sensors

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Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling.

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Modern methods for old data: An overview of some robust methods for outliers detection with applications in osteology

Cited by 7 publications

References 51 publications

Wat schaft het bot? Een vergelijkend onderzoek naar eetgewoonten en indicatoren voor sociale gelaagdheid van stedelijke en landelijke populaties in Zuid-Oost-Vlaanderen, aan de hand van stabiel isotopenonderzoek op menselijk botmateriaal

Wat schaft het bot? Een vergelijkend onderzoek naar eetgewoonten en indicatoren voor sociale gelaagdheid van stedelijke en landelijke populaties in Zuid-Oost-Vlaanderen, aan de hand van stabiel isotopenonderzoek op menselijk botmateriaal

Benefit Segmentation of Tourists to Geosites and Its Implications for Sustainable Development of Geotourism in the Southern Lake Tana Region, Ethiopia

Balancing Complex Signals for Robust Predictive Modeling

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