Abstract
Pre-processing the dataset is an important stage in the Knowledge Discovery in Datasets (KDD) process. Filtering noise through instance selection is a necessary task. With this, the risk to use misclassified and non-representative instances to train supervised classifiers is reduced. This study aims at improving the performance of the Gamma associative classifier, by introducing a novel similarity function to guide instance selection. The experimental results, over 15 datasets, include several instance selection methods, and their influence in the performance of Gamma classifier is analyzed. The effectiveness of the proposed similarity function is tested, obtaining good results according to classifier accuracy and instance retention ratio.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.