The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

随着信息技术的快速发展, 各领域的数据正以前所未有的速度产生并被广泛收集和存储,
如何实现数据的智能化处理从而利用数据中蕴含的有价值信息已成为理论和应用的研究热点 …

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 …

This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source
Java framework (GPLv3 license) that provides a number of modules to perform a wide …

Supervised text classification methods are efficient when they can learn with reasonably
sized labeled sets. On the other hand, when only a small set of labeled documents is …

Having a multitude of unlabeled data and few labeled ones is a common problem in many
practical applications. A successful methodology to tackle this problem is self-training semi …

Self-labeled technique, a paradigm of semisupervised classification (SSC), is highly
effective in alleviating the shortage of labeled data in classification tasks via an iterative self …

Deep learning (DL)-based recommender system (RS), particularly for its advances in the
recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting …