Early Classification

15 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

marccoru/elects 30 Jan 2019

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

ml-edm/ml_edm 26 Jun 2024

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

ml-edm/ml_edm 23 Aug 2024

\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

LucaMozzo/SpikingNeuralNetwork 29 Oct 2017

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 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

andrewli77/DISC 6 Oct 2020

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 28 May 2021

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

tensorflow/tpu 5 Jan 2022

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

thartvigsen/stopandhop 21 Aug 2022

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

akinori-f-ebihara/llr_saturation_problem 20 Feb 2023

Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series.