Many methods can deal with some special cases of data streams (e.g., concept drift) currently; however, these methods need to store historical data and access them repeatedly, which is inconsistent with the single-channel characteristics of data …

When using two different sets of binary classification rules on the same items, we obtain two sets of binary vectors. We can, for example, consider the case of two groups of doctors with different experiences classifying patients as diseased or …

Ordinal data are frequently encountered, e.g., in the life and social sciences. Predicting ordinal outcomes can inform important decisions, e.g., in medicine or education. Two methodological streams tackle prediction of ordinal outcomes: …

The vine pair-copula construction can be used to fit flexible non-Gaussian multivariate distributions to a mix of continuous and discrete variables. With multiple classes, fitting univariate distributions and a vine to each class lead to posterior …

Distance-based clustering is widely used to group mixed numeric and categorical data (mixed-type data), where a predefined metric is used to quantify dissimilarity or distance between data points for clustering data. However, many existing metrics …

An ultrametric Gaussian mixture model is a powerful tool for modeling hierarchical relationships among latent concepts, making it ideal for studying complex phenomena in diverse and potentially heterogeneous populations. However, in many cases …

Assuming that the subject of each scientific publication can be identified by one or more classification entities, we address the problem of determining a similarity function (distance) between classification entities based on how often two …

Compared to single-task learning (STL), multi-task learning (MTL) achieves a better generalization by exploiting domain-specific information implicit in the training signals of several related tasks. The adaptation of MTL to support vector …

Multi-class imbalanced data learning faces many challenges. Its complex structural characteristics cause severe intra-class imbalance or overgeneralization in most solution strategies. This negatively affects data learning. This paper proposes a …

Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from …

In today’s data-centric world, the significance of generated data has increased manifold. Clustering the data into a similar group is one of the dynamic research areas among other data practices. Several algorithms’ proposals exist for clustering.

Since matrix energy (ME) implies the expressive merit of collective information, a classification method based on ME has not been investigated in the existing literature, which reflects its research gap in a matrix scenario. Therefore, the purpose …

Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the …

Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they are meaningful in …

Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of Krylov subspace solvers with various preconditioning techniques have been developed. However, it …

Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and dissimilar to objects from other clusters. The fundamental question arising is whether found clusters are …

Compositional data have peculiar characteristics that pose significant challenges to traditional statistical methods and models. Within this framework, we use a convenient mode parametrized Dirichlet distribution across multiple fields of …

There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically underperform. Clustering …