Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)

TL;DR

This paper introduces a neural network-based classification method that effectively handles objects with both known and unknown features, improving accuracy in text query classification using an advanced Neurobayesian approach.

Contribution

It develops a novel method based on an enhanced Neurobayesian approach that addresses the challenge of classifying objects with mixed known and unknown features, supported by theoretical proof.

Findings

Complete resolution of misclassification for mixed feature queries

Effective classification of 20 categories in Russian text queries

Theoretical validation through a proven theorem

Abstract

The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. The proposed method was developed and trained on 1500 text queries of Promobot users in Russian to classify them into 20 categories (classes). As a result, the use of the method allowed to completely solve the problem of misclassification for queries with combining known and unknown features of the model. The theoretical substantiation of the method is presented by the formulated and proved theorem On the Model with Limited Knowledge. It states, that in conditions of limited data, an equal number of equally unknown features of an object cannot have different significance for the classification problem.