Listen: Multi-label Classification

In machine learning, we often need to categorize data that does not fit into a single, neat box. While traditional classification forces us to choose just one label, multi-label classification allows us to assign any number of labels to a single input. A search query can be both informational and local. A product review can praise a fast delivery while simultaneously criticizing the item's quality.

Instead of distributing a single probability score across all categories, a multi-label classifier calculates an independent score for each individual label. If a score passes a certain threshold, that label is applied. While many systems use a flat threshold of point-five, advanced models use techniques like Otsu's thresholding to automatically calculate a unique, optimal cutoff for each specific class.

This approach is central to several proprietary models developed by DEJAN. Their Query Intent Classifier can tag a search term with any combination of intents, from commercial and transactional to local and entertainment. Their Sentiment model evaluates text across a seven-point scale, capturing mixed emotions in a single excerpt. Additionally, their Universal Query Classifier uses multi-label assignment as its default mode to support complex, custom taxonomies.

Measuring the success of these models is unique. Because inputs can have multiple tags, standard metrics must be adapted. Teams use micro-averaging to treat every single label decision equally, or macro-averaging to give equal weight to each category, ensuring that rare labels are not overshadowed by common ones.