Kamthorn PUNTUMAPON†a) , Student Member, Thanawin RAKTHAMAMON †b) , Nonmember, and Kitsana WAIYAMAI †c) , Member SUMMARY Synthetic over-sampling is a well-known method to solve class imbalance by modifying class distribution and generating synthetic samples. A large number of synthetic over-sampling techniques have been proposed; however, most of them suffer from the over-generalization problem whereby synthetic minority class samples are generated into the majority class region. Learning from an over-generalized dataset, a classifier could misclassify a majority class member as belonging to a minority class. In this paper a method called TRIM is proposed to overcome the over-generalization problem. The idea is to identify minority class regions that compromise between generalization and overfitting. TRIM identifies all the minority class regions in the form of clusters. Then, it merges a large number of small minority class clusters into more generalized clusters. To enhance the generalization ability, a cluster connection step is proposed to avoid over-generalization toward the majority class while increasing generalization of the minority class. As a result, the classifier is able to correctly classify more minority class samples while maintaining its precision. Compared with SMOTE and extended versions such as Borderline-SMOTE, experimental results show that TRIM exhibits significant performance improvement in terms of F-measure and AUC. TRIM can be used as a preprocessing step for synthetic over-sampling methods such as SMOTE and its extended versions. key words: imbalanced data, cluster-based minority over-sampling, synthetic minority over-sampling
Multiplayer games are an important and popular game mode for networked players. Since games are played by a diverse audience, it is important to scale the difficulty, or challenge, according to the skill level of the players. However, current approaches to real-time challenge balancing (RCB) in games are only applicable to single-player scenarios. In multiplayer scenarios, players with different skill levels may be present in the same area, and hence adjusting the game difficulty to match the skill of one player may affect the other players in an undesirable way. To address this problem, we have previously developed a new approach based on distributed constraint optimization, which achieves the optimal challenge level for multiple players in real-time. The main contribution of this paper is an experiment that was performed with our new multiplayer realtime challenge balancing method applied to eco-driving. The results of the experiment suggest the effectiveness of RCB.
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