Publications

Adolescent Idiopathic Scoliosis is a spinal deformity sometimes treated using a brace. Brace treatment is successful when it prevents progression (worsening) of the spinal curve during adolescence. However, physicians' understanding of what causes progression is unclear, so a method for predicting a braced patient's risk of progression could assist physicians in planning treatment. Some such models have been proposed, but most are ill-suited for clinical use. We applied conditional fuzzy clustering to a dataset of past Scoliosis patients, to generate prototypes representative of various treatment outcomes. New patients' outcomes were predicted by comparing them to the prototypes. This model is 76-81% accurate, highly interpretable, and easily integrated into the clinic's workflow, making it a potentially valuable support tool.

Fuzzy clustering forms a cornerstone of fuzzy (granular) modeling. The clusters (prototypes) are viewed as a blueprint of the model that is further refined through a number of detailed estimation techniques. In this study, we claim that while clustering is indisputable essential to fuzzy modeling, the essence of clustering mechanisms supporting this process of information granulation is not compatible with the character of the task at hand. In modeling, the required constructs are inherently direction-sensitive (that is we clearly distinguish between input and output variables). On the other hand, fuzzy clustering is direction neutral and during the formation of the clusters does not take this into consideration. We re-formulate the clustering so that the directionality aspect can be addressed in the optimization process. This leads to a new, augmented objective function to be minimized. A detailed algorithm is derived. As the directional sensitivity of the clustering method gives rise to different numbers of clusters in the input and output space, it becomes necessary to identify a mapping between these clusters which in turn gives rise to some allocation problem. Because of its inherently combinatorial character, the proposed solution is obtained through some genetic optimization. Comprehensive experiments demonstrate the performance of the approach and compare it with some of the generic version of the FCM clustering.

This paper presents selected aspects of information processing in frames. The slots of frames are considered as taking on linguistic values represented as fuzzy sets. Matching procedures are developed and algorithms resulting within this context are proposed. The question of modeling cases involving many exemplar frames associated with a single prototype frame is posed. Subsequently the resulting model is effectively applied to fill the values missing in an exemplar frame. The optimization task involving entropy criterion enables to minimize ambiguity associated with the reconstruction problem. The resulting interval-valued fuzzy constructs are interpreted in terms of relevancy of the reconstructed information.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this study, we introduce a notion of stability of information granules. Granulation of information results in a series of chunks of information usually referred to as information granules. Information granules are basic building entities involved in the formation of a broad class of systems. Information granules are percepts — entities being perceived by humans as being essential when working with some real-world phenomena, especially describing and interacting with them. The percepts need to be comprehensible. They should also reflect the experimental evidence. All in all, they should be stable meaning that they are conceptual entities that reconcile experimental reality with the subjective and ultimately observer-based judgment about the environment. Once being stable, information granules could be viewed as architecture-independent. The proposed algorithmic environment supporting this concept dwells on the ideas of statistical inference that helps quantify stability through a nonparametric testing. The χ 2 goodness-of-fit test is used here as a validation mechanism. First, the study elaborates on the formation of information granules and concentrates on the descriptive and prescriptive ways of their design. In the sequel, it is revealed how these two ways interact with the construction of stable information granules. A number of experimental studies are also included.