2022
DOI: 10.1002/qre.3237
|View full text |Cite
|
Sign up to set email alerts
|

Evaluating classifier predictive performance in multi‐class problems with balanced and imbalanced data sets

Abstract: A major issue in classification problems arises when dealing with class imbalance, which requires the adoption of a suitable performance measure able to handle imbalanced data sets. This paper introduces the Balanced 𝐴𝐶 1 and its weighted version Balanced 𝐴𝐶 2 as classifier performance measures suitable for both balanced and imbalanced data sets. The performances of the proposed measures are compared against those of other well-known performance measures through an empirical comparison using several algori… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 69 publications
0
3
0
Order By: Relevance
“…Furthermore, macro results enable comprehensive evaluation considering the whole set of target classes, while micro-averaging considers the target classes individually. The latter is beneficial in imbalanced classification problems [74], [75], [76]. Finally, run-time is measured to compare the performance of the different models.…”
Section: ) Incremental Classificationmentioning
confidence: 99%
“…Furthermore, macro results enable comprehensive evaluation considering the whole set of target classes, while micro-averaging considers the target classes individually. The latter is beneficial in imbalanced classification problems [74], [75], [76]. Finally, run-time is measured to compare the performance of the different models.…”
Section: ) Incremental Classificationmentioning
confidence: 99%
“…Chuquin et al 4 present a straightforward extension of the K-means clustering to spatially correlated functional data, with an application in the analysis of the normalized difference vegetation index in a large region of Ecuador. From a more general perspective, Vanacore et al 5 address the problem of assessing prediction performance in the multi-class classification of imbalanced data sets. In the optimal Design of Experiments (DoE) Pesce et al 6 offer new results that are oriented to bias reduction in large datasets and protection against confounders.…”
mentioning
confidence: 99%
“…present a straightforward extension of the K‐means clustering to spatially correlated functional data, with an application in the analysis of the normalized difference vegetation index in a large region of Ecuador. From a more general perspective, Vanacore et al 5 . address the problem of assessing prediction performance in the multi‐class classification of imbalanced data sets.…”
mentioning
confidence: 99%