We investigate the issue of obtaining weights, which are associated with aggregation in fuzzy signatures, from real world data. Our approach will provide a way to extract the relevance of lower levels to the higher levels of the hierarchical fuzzy signature structure. We also handle the non-differentiability of max-min aggregation functions for gradient based learning. A mathematically proved method, which is found in the literature to approximate the derivatives of max-min functions, has been used.
Predicting reading comprehension from eye gaze data is a difficult task. We investigate the use of artificial neural networks (ANNs) to predict reading comprehension scores from eye gaze collected from participants who read and completed an online tutorial in our lab. Problems such as large feature sets and small highly imbalanced data sets compound to make this task even more complex. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed-forward neural networks to overcome these problems. We show that the use of FOE as the performance function for training ANNs provides significantly better classification of eye movements to reading comprehension scores. ANNs with three hidden layers of neurons gave the best classification results especially when FOE is used as the performance function for training. In these cases we found up to 50% reduction in misclassification rates compared to using MSE. We found that ANNs give optimal classification results in comparison to other classification techniques. When FOE is used as the performance function for training the ANNs the misclassification rates are halved compared to the other techniques. Cluster analysis was performed on one of the more complex data sets. Interesting reading behaviour properties were found within the data set. The intended use of this research is in the design of adaptive online learning environments that use eye gaze to predict user comprehension from reading behavior.
Fuzzy Signatures are hierarchical multi aggregative descriptors of objects. They have reduced computational complexity compared to formal fuzzy rule based systems. Weighted Relevance Aggregation enhances the performance of hierarchical Fuzzy Signatures. Thus, they are very robust and flexible under perturbed input data. On the other hand the Choquet Integral, which is based on fuzzy measures, is a powerful aggregation tool in multi-criteria decision making. We compared Fuzzy Signatures and the Choquet Integral as practical applications for hierarchical and non-hierarchical data aggregation/organization methods.
In this paper we present a hybrid measure of semantic word similarity using fuzzy inference system which combines both the corpus based distance measures as well as gloss overlap to get the final similarity between two words. We use WordNet as a lexical dictionary to get semantic information about words. We show that this new measure reasonably correlates to human judgments and the average performance is boosted by using triangular membership function in the output.
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