A number of visual quality measures have been introduced in visual analytics literature in order to automatically select the best views of high dimensional data from a large number of candidate data projections. These methods generally concentrate on the interpretability of the visualization and pay little attention to the interpretability of the projection axes. In this paper, we argue that in...

Many studies on modeling of fuzzy inference systems have been made. The issue of these studies is to construct automatically fuzzy systems with interpretability and accuracy from learning data based on meta-heuristic methods. Since accuracy and interpretability are contradicting issues, there are some disadvantages for self-tuning method. Obvious drawbacks of the method are lack of interpretabi...

Edward Nelson published in 1986 a book defending an extreme formalist view of mathematics according to which there is an impassable barrier in the totality of exponentiation. On the positive side, Nelson embarks on a program of investigating how much mathematics can be interpreted in Raphael Robinson’s theory of arithmetic Q. In the shadow of this program, some very nice logical investigations ...

Tsimpli and Dimitrakopoulou (2007) observed that uninterpretable features are unavailable in second language (L2) acquisition after the critical period. In this paper, we verify this claim by providing evidence from Persian speaking learners of English as an L2 on the status of resumptive pronouns (RPs) as uniterpretable features. Unlike English which does not allow RPs, Persian shows various b...

Interpretability is one of the most significant properties of Fuzzy Systems which are widely acknowledged as gray boxes against other Soft Computing techniques such as Neural Networks usually regarded as black boxes. It is essential for applications with high human interaction (decision support systems in medicine, economics, etc). The use of accuracy indices to guide the fuzzy modeling process...

Many classification algorithms suffer from a lack of human interpretability. Using such classifiers to solve real world problems often requires blind faith in the given model. In this paper we present a novel approach to classification that takes into account interpretability and visualization of the results. We attempt to efficiently discover the most relevant snapshot of the data, in the form...

Current research lines in fuzzy modeling mostly tackle with improving the accuracy in descriptive models, and the improving of the interpretability in approximative models. This paper deals with the second issue approaching the problem by means of multi-objective optimization in which accurate and interpretability criteria are simultaneously considered. Evolutionary Algorithms are specially app...