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Found 12 papers, 6 papers with code
Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts
Training machine learning models from data with weak supervision and dataset shifts is still challenging.
biquality-learn: a Python library for Biquality Learning
That is why Biquality Learning has been proposed as a machine learning framework to design algorithms capable of handling multiple weaknesses of supervision and dataset shifts without assumptions on their nature and level by relying on the availability of a small trusted dataset composed of cleanly labeled and representative samples.
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering.
Open challenges for Machine Learning based Early Decision-Making research
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
When to Classify Events in Open Times Series?
In the Early Classification in Open Time Series problem (ECOTS), the task is to predict events, i. e. their class and time interval, at the moment that optimizes the accuracy vs. earliness trade-off.
Early and Revocable Time Series Classification
Many approaches have been proposed for early classification of time series in light of itssignificance in a wide range of applications including healthcare, transportation and fi-nance.
Contrastive Representations for Label Noise Require Fine-Tuning
In presence of noise the experiments show that fine tuning of Contrastive representation allows the six methods to achieve better results than end-to-end learning and represent a new reference compare to the recent state of art.
Early Classification of Time Series is Meaningful
Many approaches have been proposed for early classification of time series in light of its significance in a wide range of applications including healthcare, transportation and finance.
Interpretable Feature Construction for Time Series Extrinsic Regression
Supervised learning of time series data has been extensively studied for the case of a categorical target variable.
From Weakly Supervised Learning to Biquality Learning: an Introduction
The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies".
Importance Reweighting for Biquality Learning
The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies", namely: poor quality, non adaptability, and insufficient quantity of labels.
Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction.
