Data reduction can increase generalization abilities of the learning model and shorten
learning time. It can be particularly helpful in analyzing big data sets. This paper focuses on …

This paper focuses on the machine classification with data reduction. The aim of the data
reduction techniques is decreasing the quantity of information required to learn a high …

In the paper, several data reduction techniques for machine learning from big datasets are
discussed and evaluated. The discussed approach focuses on combining several …

Instance selection in the supervised machine learning, often referred to as the data
reduction, aims at deciding which instances from the training set should be retained for …

The problem addressed in this paper concerns data reduction through instance selection.
The paper proposes an approach based on instance selection from clusters. The process of …

In this work we propose to use fuzzy prototypes for classification tasks. We create the
prototypes by first clusterizing, separately, the available data for each class. Then we create …

Integrated machine learning is understood as integration of the data reduction with the
learning process. Such integration allows to introduce adaptation mechanisms within the …

We introduce a logic-driven clustering in which prototypes are formed and evaluated in a
sequential manner. The way of revealing a structure in data is realized by maximizing a …

Due to the increasing size of the datasets, prototype selection techniques have been applied
for reducing the computational resources involved in data mining and machine learning …

One of the main methods in data reduction processes is the instance selection method.
Reducing the dataset has two main objectives: reducing the requirements for computing …