Multilabel classifiers are the bedrock of autonomous cars, apps like Google Lens, and intelligent assistants from Amazon’s Alexa to Google Assistant. They map input data into multiple categories at once — classifying, say, a picture of the ocean as containing “sky” and “boats” but not “desert.”

In pursuit of more computationally efficient classifiers, scientists at Amazon’s Alexa AI division recently experimented with an approach they describe in a preprint paper (“”). They claim that in tests their multilabel classification technique outperforms four leading alternatives using three data sets and demonstrates improvements on five different performance measures.

“The need for multilabel classification arises in many different contexts. Originally, it was investigated as a means of doing text classification [but since then], it’s been used for everything

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