In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

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 …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

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 …

The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …

Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …

In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is
established and its parallel implementation is developed in the framework of Map-Reduce …

Abstract The k-Nearest Neighbors classifier is a simple yet effective widely renowned
method in data mining. The actual application of this model in the big data domain is not …

The learning of imbalanced datasets is a ubiquitous challenge for researchers in the fields of
data mining and machine learning. Conventional classifiers are often biased towards the …

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …