The F-measure space is proposed to visualize classifier performance. The preference between precision and recall is controlled by a factor. A crisp classifier is represented as a curve for a range of P(+). The proposed Iterative Boolean Combination(IBC) algorithm is optimized in this space.
Aug 30, 2018 · It consists in plotting the F-measure of a given classifier versus two parameters – the level of imbalance and the preference between recall and ...
Oct 22, 2024 · The proposed F-measure space allows to visualize and compare classi- fiers' performance under different operating conditions more easily than in ...
A global evaluation space for the scalar F-measure metric that is analogous to the cost curves for expected cost is proposed, where a classifier is ...
In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of imbalance levels for the ...
This paper investigates specialized performance evaluation metrics and tools for imbalance problem, including scalar metrics that assume a given operating ...
Sep 19, 2018 · F-measure curves: A tool to visualize classifier performance under imbalance. Highlights · Logistic discrimination based on G-mean and F-measure ...
Feb 28, 2020 · This paper presents the new F-measure curves as a global visualization tool analogous to cost curves for expected cost, which consist of ...
This space allows us to visualize the global performance of the classifier over a range of decision thresholds, under different imbalance levels and the given ...
Highlights •The F-measure space is proposed to visualize classifier performance.•The preference between precision and recall is controlled by a factor.