Recent progress has been made in the field of multilabel streaming classification, where an instance can be associated with several labels simultaneously. Most recent research has focused on adapting models to the dynamic distribution of nonstationary data streams. However, continual learning is not only adaptation to concept drift: phenomena such as catastrophic forgetting, as well as forward and backward transfers, appear when new classification tasks are introduced in the data stream. This paper aims to develop a standardized and operational evaluation protocol specifically adapted to the study of these phenomena, in order to identify the most promising strategies for this new multi-label, multi-task learning problem on tabular data stream. This protocol, which is reproducible, fair, and flexible enough to allow the simulation of a large number of scenarios, includes the creation of multi-label and multi-task streams, and an evaluation protocol for measuring (a) online performance, (b) phenomena linked to continual learning and (c) resources consumed. It is tested to compare 18 continual multi-label classification strategies on 6 open literature datasets and 3 simulated datasets. This exploratory analysis has enabled us to identify the promising nature of neural networks coupled with data replay.

Extrinsic regression of time series data consists in predicting the value of a numerical target variable using an input vector which is a time series. The target variable is considered as “extrinsic” as it is not of the same nature as the series values and may not necessarily follow the temporal continuity of the series. This formalization addresses a wide range of problems in different application areas, such as environmental, health or sentiment analysis. In line with the literature on supervised classification of time series, some classification methods have been adapted to the task of regression. Existing regression methods are diverse and use different paradigms, e.g. distance-based methods, interval-based or neural network-based approaches. In parallel to these developments, several libraries for unsupervised feature extraction from time series data have been developed, primarily for descriptive analysis and visualization purposes. In this paper, we combine existing regression methods with signal processing libraries that extract features from time series. To that purpose, the potential of 10 libraries, for the extrinsic regression task, across a set of 61 datasets and six usual regressors is evaluated. The comparative analysis of results from over 3,000 learning ex-periments suggests that unsupervised feature extraction achieves competitive performance for extrinsic regression.

Time series classification has achieved significant advancements through deep learning models; however, these models often suffer from high complexity and computational costs. To address these challenges while maintaining effectiveness, we introduce COCALITE, an innovative hybrid model that combines the efficient LITE model with an augmented version incorporating Catch22 features during training. COCALITE operates with only 4.7% of the parameters of the state-of-the-art Inception model, significantly reducing computational overhead. By integrating these complementary approaches, COCALITE leverages both effective feature engineering and deep learning techniques to enhance classification accuracy. Our extensive evaluation across 128 datasets from the UCR archive demonstrates that COCALITE achieves competitive performance, offering a compelling solution for resource-constrained environments.

Hospitals face high occupation rates resulting in a longer boarding time and more complex bed management. This task could be facilitated by anticipating the unscheduled admissions. We study the capability of information from French electronic health records of an emergency department (ED) to predict patient disposition decisions. We compare the performances of five learning models in predicting the admission of a patient visiting an emergency department and in predicting the patient’s place of admission at two progressive time points throughout the ED care process: triage and initial assessment. Medical and administrative data were retrospectively collected on 53,608 visits to the Groupe Hospitalier Bretagne Sud, France, from July 2020 to June 2021. Our best model achieve a ROC-AUC equal to 88% and F1-score equal to 75% for admission prediction. Regarding medical unit admission prediction, the global ROC-AUC equals to 87% and F1-score ranges from 38% to 77% for the four admission classes, i.e., intensive care unit (6% of the dataset), medicine units (45%), surgery units (13.2%), and observation unit (35.8%). A validation with a posterior dataset indicates constant results.

Early classification of time series assigns each time series to one of a set of pre-defined classes using as few measurements as possible while preserving a high accuracy. This implies solving online the trade-off between the earliness and the prediction accuracy. This has been formalized in previous work where a cost-based framework taking into account both the cost of misclassification and the cost of delaying the decision has been proposed. The best resulting method, called Economy-$\gamma$, is unfortunately so far limited to binary classification problems. This paper presents a set of six new methods that extend the Economy-$\gamma$method in order to solve multiclass classification problems. Extensive experiments on 33 datasets allowed us to compare the performance of the six proposed approaches to the state-of-the-art one. The results show that: (i) all proposed methods perform significantly better than the state of the art one; (ii) the best way to extend Economy-$\gamma$to multiclass problems is to use a confidence score, either the Gini index or the maximum probability.

Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Then, an in-depth comparison of this semi-supervised method with the original supervised MODL approach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.