Early Classification
15 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
Early Classification of Time Series: Taxonomy and Benchmark
In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification.
ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data.
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes.
Adaptive-Halting Policy Network for Early Classification
Early classification of time series is the prediction of the class label of a time series before it is observed in its entirety.
Interpretable Sequence Classification via Discrete Optimization
Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance.
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible.
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia
We develop an evaluation framework inspired by the early classification literature, in order to quantify the tradeoff between diagnostic performance and inference time for sparse analytic approaches.
Stop&Hop: Early Classification of Irregular Time Series
We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems.
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series.