The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …

In the last few years, a large hype around the topic of Machine Learning (ML) has emerged;
this can be justified thanks to various ML approaches having recently undergone rapid …

Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

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 …

The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

Many modern methods for prediction leverage nearest neighbor search to find past training
examples most similar to a test example, an idea that dates back in text to at least the 11th …

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a
very interesting and challenging task. The application of standard data mining tools in such …

The (unheralded) first step in many applications of automated text analysis involves
selecting keywords to choose documents from a large text corpus for further study. Although …

Learning a classifier from class-imbalance data is an important challenge. Among existing
solutions, SMOTE is one of the most successful methods and has an extensive range of …