Classification assessment methods

Alaa Tharwat

doi:https://doi.org/10.1016/j.aci.2018.08.003

Classification assessment methods

2018 · Applied Computing and Informatics · 2,377 citations · 1 from inside this corpus

Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly for evaluating different learning algorithms. Most of these measures are scalar metrics and some of them are graphical methods. This paper introduces a detailed overview of the classification assessment measures with the aim of providing the basics of these measures and to show how it works to serve as a comprehensive source for researchers who are interested in this field. This overview starts by highlighting the definition of the confusion matrix in binary and multi-class classification problems. Many classification measures are also explained in details, and the influence of balanced and imbalanced data on each metric is presented. An illustrative example is introduced to show (1) how to calculate these measures in binary and multi-class classification problems, and (2) the robustness of some measures against balanced and imbalanced data. Moreover, some graphical measures such as Receiver operating characteristics (ROC), Precision-Recall, and Detection error trade-off (DET) curves are presented with details. Additionally, in a step-by-step approach, different numerical examples are demonstrated to explain the preprocessing steps of plotting ROC, PR, and DET curves.

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→Classification assessment methodsLegacy edge — source detail unavailable
Classification assessment methods→An introduction to ROC analysisLegacy edge — source detail unavailable
Classification assessment methods→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
Classification assessment methods→Learning from Imbalanced DataLegacy edge — source detail unavailable
Classification assessment methods→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
Classification assessment methods→A systematic analysis of performance measures for classification tasksLegacy edge — source detail unavailable
Classification assessment methods→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
Classification assessment methods→Basic principles of ROC analysisLegacy edge — source detail unavailable
Classification assessment methods→The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…Legacy edge — source detail unavailable
Classification assessment methods→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
Classification assessment methods→Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evalu…Legacy edge — source detail unavailable
Classification assessment methods→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→Learning from Imbalanced DataLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→An introduction to ROC analysisLegacy edge — source detail unavailable
The relationship between Precision-Recall and ROC curves→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
Learning from Imbalanced Data→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
Learning from Imbalanced Data→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
Learning from Imbalanced Data→An introduction to ROC analysisLegacy edge — source detail unavailable
An introduction to ROC analysis→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
An introduction to ROC analysis→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Learning from Imbalanced DataLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
A systematic analysis of performance measures for classification tasks→Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evalu…Legacy edge — source detail unavailable
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evalu…Legacy edge — source detail unavailable
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…Legacy edge — source detail unavailable
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→An introduction to ROC analysisLegacy edge — source detail unavailable
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→A systematic analysis of performance measures for classification tasksLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
An introduction to ROC analysis	2005	21,837
UCI Repository of machine learning databases	1998	10,546
Learning from Imbalanced Data	2009	10,209
The use of the area under the ROC curve in the evaluation of machine learning algorithms	1997	7,328
A systematic analysis of performance measures for classification tasks	2009	6,889
The relationship between Precision-Recall and ROC curves	2006	6,427
Basic principles of ROC analysis	1978	6,099
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…	2015	5,051
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…	2001	2,307
Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evalu…	2006	1,854
An insight into classification with imbalanced data: Empirical results and current trends…	2013	1,602

What cites it, inside the corpus

Paper	Year	Cited
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…	2020	6,132

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Topics

Imbalanced Data Classification Techniques	Computer Science
Text and Document Classification Technologies	Computer Science
Rough Sets and Fuzzy Logic	Computer Science

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