A new algorithm is proposed to learn fuzzy partitions with a high interpretability degree. The set of input variables, the number of linguistic terms per variable, and the type (triangular or trapezoidal) and parameters of the membership functions are learnt by means of a meta-algorithm that uses a simple learning method to generate a fuzzy rule set from the derived fuzzy partitions. Interpreta...

The ‘hnique selling point” of fuzzy systems is usually the interpretability of its rule base. However, very often only the U C C U T U C ~ of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measurz the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of...

The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this probl...

Next to prediction accuracy, interpretability is one of the fundamental performance criteria for machine learning. While high accuracy learners have intensively been explored, interpretability still poses a difficult problem. To combine accuracy and interpretability, this paper introduces an framework which combines an approximative model with a severely restricted number of features with a mor...

Our main result is the equivalence of two notions of reducibility between structures. One is a syntactical notion which is an effective version of interpretability as in model theory, and the other one is a computational notion which is a strengthening of the wellknown Medvedev reducibility. We extend our result to effective biinterpretability and also to effective reductions between classes of...

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on learning systems. It related to many ethical problems, e.g., algorithmic discrimination. Moreover, a desired property for networks become powerful tools in other research fields, drug discovery and genomics. In this survey, we con...

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse mo...

Articles in general and definite articles in particular can create problems even long after all other aspects of English have been mastered. The present article investigated the learnability problems related to the acquisition of count-mass distinction of English nominals by Persian L2 learners. The theoretical underpinning of the study is the interpretability hypothesis (Tsimpli & Dimitrakopou...