Early Classification
14 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Early Classification models and implementations
ECG200
Most implemented papers
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
marccoru/elects
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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.
Representation Learning of Tangled Key-Value Sequence Data for Early Classification
To address this problem, we propose a novel method, i. e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation.
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
Thartvigsen/EARLIEST
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KDD 2019
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
TaikiMiyagawa/MSPRT-TANDEM
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We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible.
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.
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia
tensorflow/tpu
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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.
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.
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.
