The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Multiple criteria sorting methods assign objects into ordered categories while objects are
characterized by a vector of n attributes values. Categories are ordered, and the assignment …

Feature selection methods with antinoise performance are effective dimensionality reduction
methods for classification tasks with noise. However, there are few studies on robust feature …

In many classification tasks there is a requirement of monotonicity. Concretely, if all else
remains constant, increasing (resp. ádecreasing) the value of one or more features must not …

A sentiment analysis of Arabic texts is an important task in many commercial applications
such as Twitter. This study introduces a multi-criteria method to empirically assess and rank …

Feature selection facilitates pattern recognition, and fuzzy neighborhood rough sets provide
an effective tool. By fuzzy neighborhood rough sets, we propose a heuristic feature selection …

In the course of a computational experiment on bank customer churn data, we demonstrate
the explanatory and predictive capacity of monotonic decision rules. The data exhibit a …

Monotonic classification is a common task in the field of multicriteria decision-making, in
which features and decision obey a monotonic constraint. The dominance-based rough set …

The initial productivity of directional oil wells is essential to research during the early
development stage of offshore oilfields. Since the influence factors of productivity are …