Pacc - A Discriminative and Accuracy Correlated Measure for Assessment of Classification Results

Enhancing Confusion Entropy (CEN) for binary and multiclass classification

“…A further comparison has been carried out with the Probabilistic Accuracy (PACC) introduced in [13], and the Entropy-Modulated-Accuracy EMA and the Normalized Information Transfer (NIT) factor, both introduced in [15]. We consider different examples in which sometimes PACC* = 1–PACC behaves consistently with MCC*, increasing when IN entropy decreases and decreasing when OUT decreases, while 1/NIT behaves in accordance with CEN and MCEN, decreasing in both cases, but with the handicap that unlike what happens with CEN and MCEN, NIT factor does not distinguish between IN and OUT.…”

“…Several works have considered the question of the introduction and comparison of different performance measures for classification, inspired, in one way or another, by Shannon’s entropy. For example, in [13] the authors introduce a novel measure called PACC (Probabilistic Accuracy) in the multi-class setting, making a comparative study of it with other measures as Accuracy, MCC and CEN, among others.…”

“…Next part is devoted to study the Z A family of confusion matrices. Then, we compare CEN, MCEN, Accuracy and MCC with two recently introduced measures: the Probabilistic Acuracy PACC ([13]) and the Entropy-Modulated Accuracy EMA ([14]). Finally, some experiments performed in the binary setting to compare CEN with MCEN through four real database sets are included in the Supporting Information file.…”