JUCS - Journal of Universal Computer Science 28(3): 269-291, doi: 10.3897/jucs.80747
Probability-driven scoring functions in combining linear classifiers
expand article infoPawel Trajdos, Robert Burduk
‡ Wroclaw University of Science and Technology, Wroclaw, Poland
Open Access

Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some improvement may be obtained.

Linear Classifier; Potential Function; Ensemble of Classifiers; Score Function