Discretization of numerical data is one of the most influential data preprocessing tasks in
knowledge discovery and data mining. The purpose of attribute discretization is to find …

Decision makers are often faced with several conflicting alternatives. How do they evaluate
trade-offs when there are more than three criteria? To help people make optimal decisions …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

The use of feature selection can improve accuracy, efficiency, applicability and
understandability of a learning process. For this reason, many methods of automatic feature …

In machine learning, discretization and feature selection (FS) are important techniques for
preprocessing data to improve the performance of an algorithm on high-dimensional data …

Adopting new metals 3D printers introduces time and cost obstacles to printing parts with the
same quality as was attained on existing printers. A large number of trial-and-error …

For imbalanced datasets, the focus of classification is to identify samples of the minority
class. The performance of current data mining algorithms is not good enough for processing …

Discretization algorithms have played an important role in data mining and knowledge
discovery. They not only produce a concise summarization of continuous attributes to help …

The variable precision rough sets (VPRS) model is a powerful tool for data mining, as it has
been widely applied to acquire knowledge. Despite its diverse applications in many …