Training a personal alert system for research information recommendation

Information Systems, and in particular Current Research Information Systems (CRISs), are usually quite difficult to query when looking for specific information, due to the huge amounts of data they contain. To solve this problem, we propose to use a personal search agent that uses fuzzy and rough sets to inform the user about newly available information. Additionally, in order to automate the operation of our solution and to provide it with sufficient information, a document classification module is developed and tested. This module also generates fuzzy relations between research domains that are used by the agent during the mapping process

Interval-valued algebras and fuzzy logics

In this chapter, we present a propositional calculus for several interval-valued fuzzy logics, i.e., logics having intervals as truth values. More precisely, the truth values are preferably subintervals of the unit interval. The idea behind it is that such an interval can model imprecise information. To compute the truth values of ‘p implies q’ and ‘p and q’, given the truth values of p and q, we use operations from residuated lattices. This truth-functional approach is similar to the methods developed for the well-studied fuzzy logics. Although the interpretation of the intervals as truth values expressing some kind of imprecision is a bit problematic, the purely mathematical study of the properties of interval-valued fuzzy logics and their algebraic semantics can be done without any problem. This study is the focus of this chapter

Scalable approximate FRNN-OWA classification

Fuzzy Rough Nearest Neighbour classification with Ordered Weighted Averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbour search. In practice, the query time complexity of exact nearest neighbour search algorithms in more than a handful of dimensions is near-linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbours returned by Hierarchical Navigable Small Worlds (HNSW), a recent approximative nearest neighbour search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW, and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster

A comprehensive study of implicator-conjunctor based and noise-tolerant fuzzy rough sets: definitions, properties and robustness analysis

© 2014 Elsevier B.V. Both rough and fuzzy set theories offer interesting tools for dealing with imperfect data: while the former allows us to work with uncertain and incomplete information, the latter provides a formal setting for vague concepts. The two theories are highly compatible, and since the late 1980s many researchers have studied their hybridization. In this paper, we critically evaluate most relevant fuzzy rough set models proposed in the literature. To this end, we establish a formally correct and unified mathematical framework for them. Both implicator-conjunctor-based definitions and noise-tolerant models are studied. We evaluate these models on two different fronts: firstly, we discuss which properties of the original rough set model can be maintained and secondly, we examine how robust they are against both class and attribute noise. By highlighting the benefits and drawbacks of the different fuzzy rough set models, this study appears a necessary first step to propose and develop new models in future research.Lynn D’eer has been supported by the Ghent University Special Research Fund, Chris Cornelis was partially supported by the Spanish Ministry of Science and Technology under the project TIN2011-28488 and the Andalusian Research Plans P11-TIC-7765 and P10-TIC-6858, and by project PYR-2014-8 of the Genil Program of CEI BioTic GRANADA and Lluis Godo has been partially supported by the Spanish MINECO project EdeTRI TIN2012-39348-C02-01Peer Reviewe

Finding similar research papers using language models

The task of assessing the similarity of research papers is of interest in a variety of application contexts. It is a challenging task, however, as the full text of the papers is often not available, and similarity needs to be determined based on the papers' abstract, and some additional features such as authors, keywords, and journal. Our work explores the possibility of adapting language modeling techniques to this end. The basic strategy we pursue is to augment the information contained in the abstract by interpolating the corresponding language model with language models for the authors, keywords and journal of the paper. This strategy is then extended by finding topics and additionally interpolating with the resulting topic models. These topics are found using an adaptation of Latent Dirichlet Allocation (LDA), in which the keywords that were provided by the authors are used to guide the process

Metadata impact on research paper similarity

While collaborative filtering and citation analysis have been well studied for research paper recommender systems, content-based approaches typically restrict themselves to straightforward application of the vector space model. However, various types of metadata containing potentially useful information are usually available as well. Our work explores several methods to exploit this information in combination with different similarity measures

Trust- and Distrust-Based Recommendations for Controversial Reviews

Recommender systems that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional collaborative filtering systems, provided they succeed in utilizing the additional trust and distrust information to their advantage. We compare the performance of several well-known trust-enhanced techniques for recommending controversial reviews from Epinions.com, and provide the first experimental study of using distrust in the recommendation process

A Noise-tolerant Approach to Fuzzy-Rough Feature Selection

In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative; in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual (in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued) data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well as the traditional rough set model, as special cases

Fuzzy-Rough Nearest Neighbour Classification and Prediction

AbstractNearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations from fuzzy-rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other NN approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction methods. Moreover, we show that the robustness of our methods against noise can be enhanced effectively by invoking the approximations of the Vaguely Quantified Rough Set (VQRS) model, which emulates the linguistic quantifiers “some” and “most” from natural language