Knowledge and Information Systems

Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from ...

To automatically extract data records from Web pages, the data record extraction algorithm is required to be robust and efficient. However, most of existing algorithms are not robust enough to cope with rich information or noisy data. In this paper, we ...

In this article, we propose a novel multivariate method for link prediction in evolving heterogeneous networks using a Nonlinear Autoregressive Neural Network with External Inputs (NARX). The proposed method combines (1) correlations between different ...

Robust frequent itemset mining has attracted much attention due to the necessity to find frequent patterns from noisy data in many applications. In this paper, we focus on a variant of robust frequent itemsets in which a small amount of "faults" is ...

Predicting student performance (PSP) is of great use from an educational perspective, especially for high-risk students who need timely help to complete their studies. Previous PSP studies construct prediction models mainly on data collected from ...

We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-t likelihood. The posterior distribution is approximated by a Laplace Approximation, and ...

This paper develops a Pythagorean fuzzy (PF) mathematical programming method to solve multi-attribute group decision-making problems under PF environments. The main work is summarized as four aspects: (1) Considering the fuzziness and hesitancy in ...

In this work, we focus on online review systems, in which users provide opinions about a set of entities (movies, restaurants, etc.) based on their experiences and in turn can check what others prefer. These systems have been proved to be sensitive to ...

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised ...