Naslov
Using Shadowed Clustering and Breeder GA in the Imbalanced Data Classification Problems

Autori
Panjkota, Ante ; Stančić, Ivo ; Musić, Josip ; Drole, Miha ; Vračar, Petar ; Kononenko, Igor ; Kukar, Matjaž

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 18th International Multiconference INFORMATION SOCIETY – IS 2015 Volume A (Intelligent Systems) / Piltaver, Rok ; Gams, Matjaž - Ljubljana : Institut Jožef Stefan, 2015, 88-91

Skup
INFORMATION SOCIETY – IS 2015 (Intelligent Systems)

Mjesto i datum
Ljubljana, Slovenija, 07.10.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Cluster Analysis; Feature selection; Imbalanced data classification; minority class oversampling; ensembles for classification

Sažetak
We discuss possibilities and advantages of using a novel oversampling approach based on the synthetic minority instances generation by using shadowed clustering and Breeder genetic algorithm for dealing with imbalanced classification problems. Viability of the proposed oversampling method is confirmed through comparison with state-of-the-algorithms in experiments on four imbalanced datasets obtained from a Blender-based simulator. In all conducted experiments the proposed oversampling approach performs at least as well as state-of the-art algorithms. Our results indicate a great potential for future ensemble algorithm formed from orthogonal projections and oversampled as proposed in this.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA