Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping
Ruck Thawonmas and Keita Iizuka
Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan
Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classification when the k-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.
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