Exploratory data analysis for outlier detection in bioequivalence studies

Mogoş, Bianca

doi:10.1016/j.bbe.2013.07.005

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…These include the moustache box, bar graph, quantum diagrams, histogram, run sequence plot, etc. [1], [10], [11]. In the field of Artificial Learning, the presence of outliers in training databases is a problem for the development of good predictive models for many algorithms.…”

Section: Outlier Detectionmentioning

confidence: 99%

Method for Automatically Processing Outliers of a Quantitative Variable

Kerandel¹,

Edoété²,

Odilon³

et al. 2020

IJACSA

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In data analysis processes, the treatment of outliers in quantitative variables is very critical as it affects the quality of the conclusions. However, despite the existence of very good tools for detecting outliers, dealing with them is not always straightforward. Indeed, statisticians recommend modeling the process underlying outliers to identify the best way to deal with them. In the context of Data Science and Machine Learning, the identification of processes that generate outliers remains problematic because this work requires a visual human interpretation of certain statistical tools. The techniques proposed so far, are systematic imputations by a central tendency characteristic, usually the arithmetic mean or median. Although adapted to the framework of Data Science and Machine Learning, these different approaches cause a fundamental problem, that of modifying the distribution of the initial data. The purpose of our paper is to propose an algorithm that allows the automatic processing of outliers by a software while preserving the distributional structure of the treated variable, whatever the law of probability is. The method is based on the moustache box theory developed by John Tukey. The procedure is tested with existing real data. All treatments are performed with the R programming language.

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Section: Outlier Detectionmentioning

confidence: 99%

Method for Automatically Processing Outliers of a Quantitative Variable

Kerandel¹,

Edoété²,

Odilon³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Unsupervised learning Supervised learning Statistical Others Figure 5 depicts the techniques to solve the outliers issue. Several papers make frequent use of unsupervised learning (i.e., partitional, density and hierarchical algorithms) and statistical methods [19][20][21][22][23][24]; lesser extent the supervised learning (i.e., variations of decision tree, k-nn and support vector machine algorithms) and genetic algorithms [25][26][27]. …”

Section: Outliersmentioning

confidence: 99%

A systematic review of data quality issues in knowledge discovery tasks

Corrales

Ledezma

Corrales

2016

Rev. ing. univ. Medellín

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Large volume of data is growing because organizations are continuously capturing the collective amount of data for a better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust. ResumenHay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.

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Exploratory data analysis for outlier detection in bioequivalence studies

Cited by 2 publications

References 12 publications

Method for Automatically Processing Outliers of a Quantitative Variable

Method for Automatically Processing Outliers of a Quantitative Variable

A systematic review of data quality issues in knowledge discovery tasks

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