Gradient boosting machines are a family of powerful machine-learning techniques that have
shown considerable success in a wide range of practical applications. They are highly …

Text documents usually contain high dimensional non-discriminative (irrelevant and noisy)
terms which lead to steep computational costs and poor learning performance of text …

One of the common ways to perform data-driven fault diagnosis is to employ statistical
models, which can classify the data into nominal (healthy) and a fault class or distinguish …

Feature subset selection, as a special case of the general subset selection problem, has
been the topic of a considerable number of studies due to the growing importance of data …

Hubness is an aspect of the curse of dimensionality related to the distance concentration
effect. Hubs occur in high-dimensional data spaces as objects that are particularly often …

Recent researches have shown that deep forest ensemble achieves a considerable
increase in classification accuracy compared with the general ensemble learning methods …

Gradient boosting machines, the learning process successively fits fresh prototypes to offer a
more precise approximation of the response parameter. The principle notion associated with …

Multi-label problems are challenging because each instance may be associated with an
unknown number of categories, and the relationship among the categories is not always …

The selection of discriminative terms from large quantity of terms in text documents is helpful
for achieving better accuracy of text classification. To focus on the task of selecting …

This paper proposes a technique, called Evolving Probabilistic Neural Network (ePNN), that
presents many interesting features, including incremental learning, evolving architecture, the …