Over recent decades, database sizes have grown considerably. Larger sizes present new
challenges, because machine learning algorithms are not prepared to process such large …

An important step in building expert and intelligent systems is to obtain the knowledge that
they will use. This knowledge can be obtained from experts or, nowadays more often, from …

The existence of output noise will bring difficulties to supervised learning. Noise filtering,
aiming to detect and remove polluted samples, is one of the main ways to deal with the …

Instance selection and construction methods were originally designed to improve the
performance of the k-nearest neighbors classifier by increasing its speed and improving the …

In this paper we propose and discuss several new approaches to noise-resistant training of
multilayer perceptron neural networks. Two groups of approaches: input ones, based on …

This paper discusses approaches to noise-resistant training of MLP neural networks. We
present various aspects of the issue and the ways of obtaining that goal by using two groups …

Building an accurate prediction model is challenging and requires appropriate model
selection. This process is very time consuming but can be accelerated with meta-learning …

Several approaches to joined feature and instance selection in neural network leaning are
discussed and experimentally evaluated in respect to classification accuracy and dataset …

As an important part of the" air–ground" integrated water quality monitoring system, the
inversion of water quality from unmanned airborne hyperspectral image has attracted more …

The paper presents bagging ensembles of instance selection algorithms. We use bagging to
improve instance selection. The improvement comprises data compression and prediction …