BloodBowl 2 race clustering by different playstyles

• Main Author: Ivanauskas, Tadas
• Format: Others
• Language: English
• Published: Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT) 2020
• Subjects: Machine learning; clustering; classification; feature reduction; cluster analysis; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-41540

The number of features and number of instances has a significant impact on computation time and memory footprint for machine learning algorithms. Reducing the number of features reduces the memory footprint and computation time and allows for a number of instances to remain constant. This thesis investigates the feature reduction by clustering.9 clustering algorithms and 3 classification algorithms were used to investigate whether categories obtained by clustering algorithms can be a replacement for original attributes in the data set with minimal impact on classification accuracy. The video game Blood Bowl 2 was chosen as a study subject. Blood Bowl2 match data was obtained from a public database The results show that the cluster labels cannot be used as a substitute for the original features as the substitution had no effect on the classifications. Furthermore, the cluster labels had relatively low weight values and would be excluded by activation functions on most algorithms.