Ciro Castiello

Interpretability assessment of fuzzy knowledge bases

Computing with words (CWW) relies on linguistic representation of knowledge that is processed by operating at the semantical level defined through fuzzy sets. Linguistic representation of knowledge is a major issue when fuzzy rule based models are acquired from data by some form of empirical learning. Indeed, these models are often requested to exhibit interpretability, which is normally evaluated in terms of structural features, such as rule complexity, properties on fuzzy sets and partitions. In this paper we propose a different approach for evaluating interpretability that is based on the notion of cointension. The interpretability of a fuzzy rule-based model is measured in terms of cointension degree between the explicit semantics, defined by the formal parameter settings of the model, and the implicit semantics conveyed to the reader by the linguistic representation of knowledge. Implicit semantics calls for a representation of users knowledge which is difficult to externalise. Nevertheless, we identify a set of properties -- which we call “logical view” -- that is expected to hold in the implicit semantics and is used in our approach to evaluate the cointension between explicit and implicit semantics. In practice, a new fuzzy rule base is obtained by minimising the fuzzy rule base through logical properties. Semantic comparison is made by evaluating the performances of the two rule bases, which are supposed to be similar when the two semantics are almost equivalent. If this is the case, we deduce that the logical view is applicable to the model, which can be tagged as interpretable from the cointension viewpoint. These ideas are then used to define a strategy for assessing interpretability of fuzzy rule-based classifiers (FRBCs). The strategy has been evaluated on a set of pre-existent FRBCs, acquired by different learning processes from a well-known benchmark dataset. Our analysis highlighted that some of them are not cointensive with users knowledge, hence their linguistic representation is not appropriate, even though they can be tagged as interpretable from a structural point of view.

Knowledge discovery by a neuro-fuzzy modeling framework

In this paper a neuro-fuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base, through a two-phase learning of a neuro-fuzzy network. In order to obtain reliable and readable knowledge, two further stages are integrated with the knowledge extraction procedure: a pre-processing stage, performing variable selection on the available data to obtain simpler and more reliable fuzzy rules, and a post-processing stage, that granulates outputs of the extracted fuzzy rules so as to provide a validity range of estimated outputs. Moreover, the framework can address complex multi-input multi-output problems. In such case, two distinct modeling strategies can be followed with the opportunity of producing both a single MIMO model or a collection of MISO models. The proposed framework is verified on a real-world case study, involving prediction of chemical composition of ashes produced by combustion processes carried out in thermo-electric generators located in Italy.

Meta-data: characterization of input features for meta-learning

Common inductive learning strategies offer the tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome limitations of such base-learning approaches, a novel research trend is oriented to explore the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This could lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. As a significant set of I/O data is needed for efficient base-learning, appropriate meta-data characterization is of crucial importance for useful meta-learning. In order to characterize meta-data, firstly a collection of meta-features discriminating among different base-level tasks should be identified. This paper focuses on the characterization of meta-data, through an analysis of meta-features that can capture the properties of specific tasks to be solved at base level. This kind of approach represents a first step toward the development of a meta-learning system, capable of suggesting the proper bias for base-learning different specific task domains.

Interpretability of Fuzzy Systems: Current Research Trends and Prospects

Fuzzy systems are universally acknowledged as valuable tools to model complex phenomena while preserving a readable form of knowledge representation. The resort to natural language for expressing the terms involved in fuzzy rules, in fact, is a key factor to conjugate mathematical formalism and logical inference with human-centered interpretability . That makes fuzzy systems specifically suitable in every real-world context where people are in charge of crucial decisions. This is because the self-explanatory nature of fuzzy rules profitably supports expert assessments. Additionally, as far as interpretability is investigated, it appears that (a) the simple adoption of fuzzy sets in modeling is not enough to ensure interpretability; (b) fuzzy knowledge representation must confront the problem of preserving the overall system accuracy, thus yielding a trade-off which is frequently debated. Such issues have attracted a growing interest in the research community and became to assume a central role in the current literature panorama of computational intelligence. This chapter gives an overview of the topics related to fuzzy system interpretability, facing the ambitious goal of proposing some answers to a number of open challenging questions: What is interpretability? Why interpretability is worth considering? How to ensure interpretability, and how to assess (quantify) it? Finally, how to design interpretable fuzzy models?

Document page segmentation using neuro-fuzzy approach

In this work, we propose a new document page segmentation method, capable of differentiating between text, graphics and background, using a neuro-fuzzy methodology. Our approach is based firstly on the analysis of a set of features extracted from the image, available at different resolution levels. An initial segmentation is obtained by classifying the pixels into coherent regions, which are successively refined by the analysis of their shape. The core of our approach relies on a neuro-fuzzy methodology, for performing the classification processes. The proposed strategy is capable of describing the physical structure of a page in an accurate way and proved to be robust against noise and page skew. Additionally, the knowledge-based neuro-fuzzy methodology allows us to understand the classification mechanisms better, contrary to what happens when other kinds of knowledge-free methods are applied.

Evolutionary Neuro-Fuzzy Systems and Applications

In recent years, the use of hybrid soft computing methods has shown that in various applications the synergism of several techniques is superior to a single technique. For example, the use of a neural fuzzy system and an evolutionary fuzzy system hybridises the approximate reasoning mechanism of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. Evolutionary neural systems hybridise the neurocomputing approach with the solution-searching ability of evolutionary computing. Such hybrid methodologies retain limitations that can be overcome with full integration of the three basic soft computing paradigms, and this leads to evolutionary neural fuzzy systems. The objective of this chapter is to provide an account of hybrid soft computing systems, with special attention to the combined use of evolutionary algorithms and neural networks in order to endow fuzzy systems with learning and adaptive capabilities. After an introduction to basic soft computing paradigms, the various forms of hybridisation are considered, which results in evolutionary neural fuzzy systems. The chapter also describes a particular approach that jointly uses neural learning and genetic optimisation to learn a fuzzy model from the given data and to optimise it for accuracy and interpretability.

Modeling User Preferences through Adaptive Fuzzy Profiles

Adaptive software systems are systems that tailor their behavior to each user on the basis of a personalization process. The efficacy of this process is strictly connected with the possibility of an automatic detection of preference profiles, through the analysis of the users’ behavior during their interactions with the system. The definition of such profiles should take into account imprecision and gradedness, two features that justify the use of fuzzy sets for their representation. This paper proposes a model for representing preference profiles through fuzzy sets. The model’s strategy for adapting profiles to user preferences is to record the sequence of accessed resources by each user, and to update preference profiles accordingly so as to suggest similar resources at next user accesses. Profile adaption is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource suggestion. Simulation results are reported to show the effectiveness of the proposed approach.

Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning

Common inductive learning strategies offer tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome the limitations of such base-learning approaches, a research trend explores the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This may lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. In this paper, we present a meta-learning framework called Mindful (Meta INDuctive neuro-FUzzy Learning) which is founded on the integration of connectionist paradigms and fuzzy knowledge management. Due to its peculiar organisation, Mindful can be exploited on different levels of application, being able to accumulate learning experience in cross-task contexts. This specific knowledge is gathered during the meta-learning activity and it is exploited to suggest parametrisation for future base-learning tasks. The evaluation of the Mindful system is detailed through an ensemble of experimental sessions involving both synthetic domains and real-world data.

LEARNING FUZZY USER PROFILES FOR RESOURCE RECOMMENDATION

Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.

Fuzzy User Profiling in e-Learning Contexts

The research activity described in this paper concerns the personalisation process in e-learning contexts. Particular emphasis is laid on the mechanisms of user profiling and association between user profiles and pedagogical resources. A particular profiling model is proposed where both the pedagogical resources and the user profiles are described in terms of a fuzzy valued metadata specification. The adoption of specific fuzzy operators enables the proposed model to perform associations with a high degree of flexibility, yielding a customised resource allocation for each user.