# ClassiNet -- Predicting Missing Features for Short-Text Classification

arXiv: https://arxiv.org/abs/1804.05260 · HTML: https://mlsanity.com/paper/1804.05260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Prompt-Learning for Short Text Classification](https://mlsanity.com/paper/2202.11345/) (2022)

## Abstract (arXiv)

The fundamental problem in short-text classification is feature sparseness -- the lack of feature overlap between a trained model and a test instance to be classified. We propose ClassiNet -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex vi in the ClassiNet where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge eij connecting a vertex vi to a vertex vj represents the conditional probability that given vi exists in an instance, vj also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance vecx, we find similar features from ClassiNet that did not appear in vecx, and append those features in the representation of vecx. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.