Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning Deadline for Manuscript Submission:February 1, 2021

摘要

A vital part of proposing new machine learning and data mining approaches is evaluating them empirically to allow an assessment of their capabilities. Numerous choices go into setting up such experiments: how to choose the data, how to preprocess them (or not), potential problems associated with the selection of datasets, what other techniques to compare to (if any), what metrics to evaluate, etc. and last but not least how to present and interpret the results. Learning how to make those choices on-the-job, often by copying the evaluation protocols used in the existing literature, can easily lead to the development of problematic habits. Numerous, albeit scattered, publications have called attention to those questions and have occasionally called into question published results, or the usability of published methods.