Improving player's game experience has always been the common goal of video game practitioner. In order to get a better understanding of player's perception of game experience, we carry out experimental study for data collection and present game experience prediction model based on machine learning method. The model is trained on the proposed multi-modal database which contains: physiological modality, behavioral modality and meta-information to predict the player game experience in terms of difficulty, immersion and amusement. By investigating the model trained on separate and fusion feature sets, we show that physiological modality is effective. Moreover, better understanding is achieved with further analysis on the most relevant features in the behavioral and meta-information features set. We argue that combining the physiological modalities with behavioral and meta information can provide a better performance on the game experience prediction.

In this paper a new on-line algorithm is proposed (the Droplets algorithm) for dealing with concept drifts and to produce reliable predictions. The two main characteristics of this algorithm are that it is able to adapt to different types of drifts without making any assumptions regarding their type or when they occur, and can provide reliable predictions in a non-stationary environment without using a fixed confidence threshold. Experimental results on five datasets based on Random RBF and Rotating Hyperplane generators as well as a new semi-synthetic dataset based weather temperatures show that, by discarding difficult observations, the Droplets algorithm manages to obtain the best average accuracy against ten classifiers. The results also indicate that the algorithm manages to provide reliable prediction by accurately distinguishing which observations are easily classifiable.

We propose an extension of the spherical K-means algorithm to deal with settings where the number of data points is largely inferior to the number of dimensions. We assume the data to lie in local and dense regions of the original space and we propose to embed each cluster into its specific ellipsoid. A new objective function is introduced, analytical solutions are derived for both the centroids and the associated ellipsoids. Furthermore, a study on the complexity of this algorithm highlights that it is of same order as the regular K-means algorithm. Results on both synthetic and real data show the efficiency of the proposed method.

In this paper, a study is presented to explore ensembles of fuzzy decision trees. First of all, a quick recall of the state of the art related to ensembles of (fuzzy) decision trees in Machine Learning is presented. Afterwards, a new approach to construct a forest of fuzzy decision trees is proposed. Two experiments are described, one with forests of fuzzy decision trees, and the other with bagging of fuzzy decision trees. The results highlight the interest of using fuzzy set theory in this kind of approaches.

In this paper, we propose a new algorithm to infer automatically a fuzzy partition for the universe of a set of fuzzy values, when each of these values is associated with a class. This algorithm can be used in a fuzzy decision tree system to extract knowledge from a database and to construct a set of fuzzy rules. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.