In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study …

Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no …

Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for …

Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several …

One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select a small number of the relevant, nonredundant features to preserve the complete information …

We introduce MCCE: $${{{\underline{\varvec{M}}}}}$$ M ̲ onte $${{{\underline{\varvec{C}}}}}$$ C ̲ arlo sampling of valid and realistic $${{{\underline{\varvec{C}}}}}$$ C ̲ ounterfactual $${{{\underline{\varvec{E}}}}}$$ E ̲ xplanations for tabular …

Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has been trained and the …

Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient to reflect the training data distribution, it will often further restrict the detection …

Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature …

Artificial intelligence applications could be more powerful and comprehensive by incorporating the ability of inference, which could be achieved by probabilistic inference over implicit relations. It is significant yet challenging to represent …

Deep learning has been applied successfully in sequence understanding and translation problems, especially in univariate, unimodal contexts, where large number of supervision data are available. The effectiveness of deep learning in more complex …

Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents …

The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success …

The Brunelleschi’s Dome is one of the most iconic symbols of the Renaissance and is among the largest masonry domes ever constructed. Since the late 17th century, first masonry cracks appeared on the Dome, giving the start to a monitoring …

Given a long time series, the distance profile of a query time series computes distances between the query and every possible subsequence of a long time series. MASS (Mueen’s Algorithm for Similarity Search) is an algorithm to efficiently compute …

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their …

The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous centrality measures have been presented to characterize what is a central node. However, few studies …

Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph Completion (KGC), defined as inferring missing entities or relations based on observed facts, has …