A new approach for instance selection: Algorithms, evaluation, and comparisons
作者：
Highlights：
• We design two new algorithms using global density, relevant, irrelevant functions.
• We develop a toolkit and its GUI, management and validation capabilities.
• We evaluate and test the performance of our algorithms in terms of four metrics.
• The experimental results prove our algorithms outperform density-based approaches.
• We test the scalability and compute the polynomial-time complexity of algorithms.
摘要
•We design two new algorithms using global density, relevant, irrelevant functions.•We develop a toolkit and its GUI, management and validation capabilities.•We evaluate and test the performance of our algorithms in terms of four metrics.•The experimental results prove our algorithms outperform density-based approaches.•We test the scalability and compute the polynomial-time complexity of algorithms.
论文关键词：Big data,Data mining,Instance selection,Global density function,Time complexity
论文评审过程：Received 20 October 2018, Revised 1 September 2019, Accepted 5 February 2020, Available online 7 February 2020, Version of Record 20 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113297
