Classifiers With a Reject Option for Early Time-Series Classification

TL;DR

This paper introduces a novel ensemble classifier with a reject option for early time-series classification, enabling rapid and accurate odor detection without waiting for full signal data.

Contribution

It presents a new classifier architecture that uses ensemble agreement for early decision-making in time-series data, applied to odor classification in electronic noses.

Findings

The proposed method achieves earlier classification with high accuracy.

Experimental results show improved robustness over standard classifiers.

The approach is effective in dynamic, real-time environments.

Abstract

Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the "agreement" of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives.