Affective gaming is a hot field of research that exploits human emotion for the enhancement of player's experience during gameplay. Physiological signal is an effective modality that can provide a better understanding of the emotional states and is very promising to be applied to affective gaming. Most physiological-based affective gaming applications evaluate player's emotion on an overall game fragment. These approaches fail to capture the emotion change in the dynamic game context. In order to achieve a better understanding of psychophysiological response with a better time sensitivity, we present a study that evaluates the psychophysiological responses related to the game events. More specifically, we present a multi-modal database DAG that contains peripheral physiological signals (ECG, EDA, respiration, EMG, temperature), accelerometer signals, facial and screening recordings as well as player's self-reported eventrelated emotion assessment through game playing. We then investigate physiological-based emotion detection and recognition by using machine learning techniques. Common challenges for physiological-based affective model such as signal segmentation, feature normalization, relevant features are addressed. We also discuss factors that influence the performance of the affective models.

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 comparison of the behaviour of fuzzy decision trees and gradual fuzzy decision trees is presented in a real-world application in the context of labour economics. The aim of this study is on one hand to present, in a real case, the good property of interpretability of such decision trees. On the other hand, it shows the importance to take into account a graduality relation between attributes and the class during the construction of a fuzzy decision tree. The obtained results illustrate the differences between the two types of fuzzy decision trees.

Time series shapelets proposes an approach to extract subsequences most suitable to discriminate time series belonging to distinct classes. Computational complexity is the major issue with shapelets: the time required to identify interesting subsequences can be intractable for large cases. In fact, it is required to evaluate all the subsequences of all the time series of the training dataset. In the literature, improvements have been proposed to accelerate the process, but few provide a solution that dramatically reduces the time required to find a solution. We propose a random-based approach that reduces the time necessary to find a solution, in our experimentation until 3 orders of magnitude compared to the original method. Based on extensive experimentations on several data sets from the literature, we show that even with a few time available, random-shapelet algorithm is able to find very competitive shapelets.

The cross-listing mechanism enables that companies collect funds and investors invest in capital markets of foreign countries. Among the objectives are the increase in the liquidity, the reduction of the risk and of the capital cost. In this context, this paper analyses the relationship of dually listed stocks of Brazilian companies, simultaneously traded on the São Paulo stock Exchange and New York Exchange, through American Depositary Receipts (ADR). In this sense, we evaluate which of the markets has the greatest influence on the pricing of those assets. For this purpose, we extract knowledge in the form of linguistic summaries representing attribute co-variations, enriched by different types of additional information which characterize the context and describe the co-variation type.

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Gradual itemsets of the form “the more/less A, the more/less B” extract knowledge in the form of correlations between attributes. The methods for extracting such itemsets can generate contradictory itemsets, for example simultaneously producing the itemsets “the more A, the more B” and “the more A, the less B”. To process these contradictions, we propose a constrained definition of the gradual itemset support. In particular, it does not only depend on the considered itemset, but also on its potential contradictors. An algorithm to efficiently compute the proposed global proper gradual support is defined, as well as two methods for extracting frequent gradual itemsets according to this new support definition. Experimental results obtained from a real dataset highlight the relevance of the approach.

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, we introduce a fuzzy extension of a class of measures to compare clustering structures, namely, measures that are based on the number of concordant and the number of discordant pairs of data points. This class includes the well-known Rand index but also commonly used alternatives, such as the Jaccard measure. In contrast with previous proposals, our extension exhibits desirable metrical properties. Apart from elaborating on formal properties of this kind, we present an experimental study in which we compare different fuzzy extensions of the Rand index and the Jaccard measure.

In this paper, we present an original fuzzy interpolation method. In contrast to existing approaches, our method is able to always construct an interpolated fuzzy interval without a need of a special step dedicated to the "standardization" of non viable solutions, which fractures the sense of the interpolation. In fact, these "standardization" steps imply that, for instance, a point obtained from the interpolation of the upper limit (right side) of the fuzzy sets, is used to build the lower limit (left side) of the interpolated conclusion, breaking the underlying hypothesis of (linear) graduality. To achieve the direct interpolation, our method is based on the deviation of the observation from the expected linearly interpolated solution and constrains of the constructed solution between extreme cases. We illustrate and discuss the behavior of our method by comparison to other well known fuzzy interpolation methods.

Fuzzy gradual rules of the form the more X is A, the more Y is B linguistically express information about the correlation between attributes and their co-variation. They thus provide valuable information summarizing the trends observed in a given data set. In this paper, we consider strengthened fuzzy gradual rules, i.e. gradual rules enriched with a clause introduced by the expression “all the more”: such rules of the form the more X is A, the more Y is B, all the more Z is C offer additional precisions on the relation between the attributes. We study the definition of such strengthened rules, discussing their possible semantics, considering several interpretations of fuzzy gradual rules. We then propose quality criteria as well as a mining algorithm.

Given the very ambiguous and imprecise nature of sentiments and of their expressions, this survey focuses on approaches making use of components of graduality in the task of automatic sentiments analysis. To that aim, we review methods taking account of intrinsic psychological models components of graduality as well as extrinsic components issued from computational intelligence approaches. In particular, beyond psychological models of sentiments that define affective states as multidimensional vectors in affective continuous spaces, we identify three components of graduality, namely composition or blending, intensity and inheritance. In our discussion, we review how fuzzy set theory as well as other gradual structures based on a vectorial representation are employed to describe affective states as complex or imprecise entities. Finally, we focus on verbal expressions of sentiments and more specifically, we discuss the use of components of graduality in order to deal with sentiments complex and subtle expressions issued from the expressive power of natural languages.

We propose the use of a knowledge based framework for diagnosis in which the knowledge base consists of particular instances of general hierarchical disorder models. We study how to select which manifestation (symptom, malfunction) to query in order to reduce a set of competing diagnosis hypotheses (disorders), none of them completely satisfying, considering only the observed manifestations. We propose to use general information about the order in which competing disorder models should be probed first to guide us on the task of selecting which particular disorder instances to try to confirm first. We propose to then order which manifestation instances to probe, the presence or absence of which will help us to either confirm or eliminate that hypothesis, according to the principle that "(manifestation) instances that share some characteristics with the instance of manifestation that generated the whole process, but which completely disagree in relation to other characteristics" should be probed first.

We propose the use of a knowledge based framework for diagnosis in which the knowledge base consists of particular instances of general disorder models. We study how to select which manifestation (symptom, malfunction) to query in order to reduce a set of competing diagnosis hypotheses (disorders), none of them completely satisfying, considering only the observed manifestations. We propose to use similarity relations to guide us on the task of selecting which manifestations to further investigate in order to confirm or eliminate a hypothesis, as well as information about the order in which the competing hypotheses should be probed first.

A similarity measure between the focal elements used on a distance function of two basic belief assignments in the theory of evidence is presented, making way for the application of classical classification algorithms in this field. The properties of this measure are particular to its context, considering the characteristics of the focal elements, their relationship with each other and their proximity to the vacuous belief function that represents the state of total ignorance

In this paper, we present a new method to construct fuzzy prototypes of heterogeneous classes, in a supervised learning context. Heterogeneous classes are classes where the coexistence of far behaviours can be observed. Our approach consists in two stages. The first one enables to discover, in an original method, the different behaviours within a class by decomposing it in subclasses. In the second stage, we construct a fuzzy prototype for each subclass by using typicality degrees. Thanks to this decomposition of a class and to this characterization of typical behaviours, we propose an intuitive summarization of a class. We illustrate the advantages of our method on both artificial and real dataset.

We first introduce the fuzzy similarity measures in the context of a CBIR system. This leads to the observation of an invariance in the ranking for different similarity measures. We then propose an explanation to this phenomenon, and a larger theory about order invariance for fuzzy similarity measures. We introduce a definition for equivalence classes based on order conservation between these measures. We then study the consequences of this theory on the evaluation of document retrieval by fuzzy similarity.

We propose a new method to use an incomplete rule base with imprecise descriptions of variables. We extend classical interpolative reasoning to this case, under the assumption of graduality in variations of the variables, by using an analogical fuzzy approach.

Standard model of fuzzy computations is based on extension principle. It is known to work well, in practice, only for continuous fuzzy numbers, while producing unintuitive results when one or more arguments are discrete. It is also computationally cumbersome for all but linear operations. Another model was proposed for trapezoidal numbers only. Its operations amount to computing on the four vertices of the trapezoids, and then spanning a new trapezoid on the four resulting vertices. It is efficient, but produces fairly crude approximations for curvilinear fuzzy numbers; moreover, it is not applicable when discrete arguments are present. A model based on approximating fuzzy numbers, whether continuous or discrete, by multitrapezoidal curves and then performing coordinate-wise computations was proposed first by Ramer. It was applied to economical decision problems by his doctoral student James Wang. In this paper we place this computational method in context of fuzzy interpolations. We show how interpolation can bring quite disparate argument into a standardized form, thus permitting for efficient computations and avoid unintuitive results. Here we use the model of multiple trapezoids, but other classes of curves can be considered.