Deep learning Implementation: Multilabel Classifier

No Bullshit: Multi-label Text classification Using Keras

Discusses Implementation details with code

The goal of this blog is to dig right into the weeds of implementing a multilabel text classifier using Keras. If you have clicked on this story, I assume you are familiar with the problem formulation and how it differs from a traditional multiclass classifier. So let’s cut right to the chase.

https://thedatamint.github.io/pages/meraki-hues-art-work.html

Implementation notes:

• For a multilabel text classifier, for each training example, we have multiple labels. As a result, LabelBinarizer should be replaced by MultiLabelBinarizer.
• ‘‘Sigmoid’’ activation should be used in the final layer instead of “Softmax” activation.
• Each class in a multilabel setup is assumed to be a Bernoulli random variable and hence we shall use binary_cross_entropy loss function instead of acategorical_cross_entropy loss.
• Finally, the choice of evaluation metric is pivotal while training a multilabel classifier. Traditional evaluation metrics like accuracy do NOT make sense in the multilabel universe. We shall leverage a custom evaluation metric called α- Evaluation metric introduced by Boutell et. al. in Learning multi-label scene classification.

That's all you need to know to train a robust neural network for solving the multilabel classification problem.

Implementation:

Final Thoughts:

• This blog post aims at laying out the deal-breaking differentiators between a multiclass classifier and a multilabel classifier.
• If you are looking for a more comprehensive discussion on various aspects of a Multilabel classifier, I would encourage you to check out the blogs mentioned below.

Multi-Label Text Classification Using Keras

A Survey of Evaluation Metrics for MultiLabel Classification

• As always, feel free to reach out to me via LinkedIn if you have any questions or feedback.

Pritish Sunil Jadhav - Senior Data Science Engineer - Perpetua | LinkedIn