There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

Data preprocessing is an important step for designing classification model. Normalization is
one of the preprocessing techniques used to handle the out-of-bounds attributes. This work …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Existing aspect-based/category sentiment analysis methods have shown great success in
detecting sentiment polarity toward a given aspect in a sentence with supervised learning …

Many instance selection algorithms have been introduced to reduce the high storage
requirements and computation complexity of K-nearest neighbor (K-NN) classification rules …

Abstract Machine Learning studies often involve a series of computational experiments in
which the predictive performance of multiple models are compared across one or more …

In meta-learning, classification problems can be described by a variety of features, including
complexity measures. These measures allow capturing the complexity of the frontier that …