Learning from imbalanced data sets is known to be a challenging task. There are many
proposals to tackle the challenge for classification problems, but regarding regression the …

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

Abstract Predictive performance of Machine Learning (ML) models rely on the quality of data
used for training the models. However, if the training data is not balanced among different …

The classification task usually works with flat and batch learners, assuming problems as
stationary and without relations between class labels. Nevertheless, several real-world …

K-nearest neighbor rule (KNN) has been regarded as one of the top 10 methods in the field
of data mining. Due to its simplicity and effectiveness, it has been widely studied and applied …

Most existing imbalanced data classification models mainly focus on the classification
performance of majority class samples, and many clustering algorithms need to manually …

The k-nearest neighbor (kNN) classifier is a classical classification algorithm that has been
applied in many fields. However, the performance of the kNN classifier is limited by a simple …

In clustering process, fuzzy partition performs better than hard partition when the boundaries
between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less …

In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …

Meta-learning is increasingly used to support the recommendation of machine learning
algorithms and their configurations. These recommendations are made based on meta-data …