Evaluation of classifier performance and the impact of learning algorithm parameters
Year: 2003
Type: dissertation
Abstract: Much research has been done in the fields of classifier performance evaluation and optimization. This work summarizes this research and tries to answer the question if algorithm parameter tuning has more impact on performance than the choice of algorithm. An alternative way of evaluation; a measure function is also demonstrated. This type of evaluation is compared with one of the most accepted methods; the cross-validation test. Experiments, described in this work, show that parameter tuning often has more impact on performance than the actual choice of algorithm and that the measure function could be a complement or an alternative to the standard cross-validation tests.
Author: Niklas Lavesson
Institution: Blekinge Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W322305381