pyCLAMs: An integrated Python']Python toolkit for classifiability analysis

被引：0

作者：

Zhang, Yinsheng ^{[1
]}

Wang, Haiyan ^{[1
]}

Cheng, Yongbo ^{[2
]}

Qin, Xiaolin ^{[3
]}

机构：

[1] Zhejiang Gongshang Univ, Sch Management & Business, Hangzhou 310018, Peoples R China

[2] Nanjing Univ Finance & Econ, Sch Management Sci & Engn, Nanjing 210023, Peoples R China

[3] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Peoples R China

来源：

SOFTWAREX | 2022年 / 18卷

基金：

中国国家自然科学基金;

关键词：

Classifiability analysis; Discriminative task; !text type='Python']Python[!/text; INFORMATION; COMPLEXITY;

D O I：

10.1016/j.softx.2022.101007

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In data-driven discriminative tasks, classifiability analysis is an often-neglected and implicit step. It answers the fundamental question: does the dataset possess sufficient between-class differences? To measure the dataset's classifiability degree, we develop pyCLAMs (python package for CLassifiabilty Analysis Metrics). pyCLAMs has integrated existing classifiability complexity metrics (e.g., Fisher discriminant ratio, overlapping region volume, distribution topology) and extends more metrics/statistics, such as BER (Bayes error rate, irreducible error), ES (effect size), Person's r, Spearman's rho, Kendall's tau, IG (information gain, mutual information), ANOVA (Analysis of Variance), MANOVA (Multivariate ANOVA), MWW (Mann-Whitney-Wilcoxon test), KS (Kolmogorov-Smirnov test), etc. The current version of pyCLAMs supports 68 metrics. We recommend researchers use pyCLAMs for a precursory assessment for their classification tasks. (C) 2022 The Author(s). Published by Elsevier B.V.

引用

页数：9