Instance spaces for machine learning classification
Mario A Munoz, Laura Villanova, Davaatseren Baatar, Kate Smith-Miles
MACHINE LEARNING | SPRINGER | Published : 2018
This paper tackles the issue of objective performance evaluation of machine learning classifiers, and the impact of the choice of test instances. Given that statistical properties or features of a dataset affect the difficulty of an instance for particular classification algorithms, we examine the diversity and quality of the UCI repository of test instances used by most machine learning researchers. We show how an instance space can be visualized, with each classification dataset represented as a point in the space. The instance space is constructed to reveal pockets of hard and easy instances, and enables the strengths and weaknesses of individual classifiers to be identified. Finally, we ..View full abstract
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Awarded by Australian Research Council
This work is funded by the Australian Research Council through Australian Laureate Fellowship FL140100012. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.