141 citations · 597 across the 22 of their papers we have counts for
14 papers · 1 filter
Shapley Chains: Extending Shapley Values to Classifier Chains
Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch +1
In spite of increased attention on explainable machine learning models, explaining multi-output predictions has not yet been extensively addressed. Methods that use Shapley values…
Transferable Deep Metric Learning for Clustering
Simo Alami. C, Rim Kaddah, Jesse Read
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric…
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
Ekaterina Antonenko, Jesse Read
Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is…
Linear TreeShap
Peng Yu, Chao Xu, Albert Bifet +1
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting…
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Eran Zvuloni, Jesse Read, Antônio H. Ribeiro +2
Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to fea…
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
F. Llorente, L. Martino, J. Read +1
This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy. This type of problem can…
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Heitor Murilo Gomes, Maciej Grzenda, Rodrigo Mello +3
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning p…
Probabilistic Regressor Chains with Monte Carlo Methods
Jesse Read, Luca Martino
A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predi…
Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation
Jesse Read
Learning from data streams is an increasingly important topic in data mining, machine learning, and artificial intelligence in general. A major focus in the data stream literature…
Scikit-Multiflow: A Multi-output Streaming Framework
Jacob Montiel, Jesse Read, Albert Bifet +1
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization o…
Multi-label Methods for Prediction with Sequential Data
Jesse Read, Luca Martino, Jaakko Hollmén
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of cla…
Data Stream Classification using Random Feature Functions and Novel Method Combinations
Diego Marrón, Jesse Read, Albert Bifet +1
Big Data streams are being generated in a faster, bigger, and more commonplace. In this scenario, Hoeffding Trees are an established method for classification. Several extensions e…
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
Jesse Read, Luca Martino, David Luengo
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional…