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Lost in the Forest: Encoding categorical variables and the absent levels problem
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...
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Category-Level Contrastive Learning for Unsupervised Hashing in Cross-Modal Retrieval
Unsupervised hashing for cross-modal retrieval has received much attention in the data mining area. Recent methods rely on image-text paired data to...
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Time series clustering with random convolutional kernels
Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its...
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A comparative study of methods for estimating model-agnostic Shapley value explanations
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions...
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Interpretable linear dimensionality reduction based on bias-variance analysis
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should...
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An Overview Based on the Overall Architecture of Traffic Forecasting
With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is...
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Binary quantification and dataset shift: an experimental investigation
Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is...
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Online concept evolution detection based on active learning
Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient...
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Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network
Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion...
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A Meta-adversarial Framework for Cross-Domain Cold-Start Recommendation
The cold-start problem in recommender systems has been facing a great challenge. Cross-domain recommendation can improve the performance of...
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Leveraging Semantic Information for Enhanced Community Search in Heterogeneous Graphs
Community search (CS) is a vital research area in network science that focuses on discovering personalized communities for query vertices from...
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FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works...
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Marginal effects for non-linear prediction functions
Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as...
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Learning a Bayesian network with multiple latent variables for implicit relation representation
Artificial intelligence applications could be more powerful and comprehensive by incorporating the ability of inference, which could be achieved by...
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MMA: metadata supported multi-variate attention for onset detection and prediction
Deep learning has been applied successfully in sequence understanding and translation problems, especially in univariate, unimodal contexts, where...
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Structural learning of simple staged trees
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are...
