Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels

TL;DR

This paper introduces a novel ensemble-based method for detecting new or unseen classes in multi-class datasets with incomplete training labels, outperforming existing approaches on image datasets.

Contribution

The paper presents a new ensemble classifier approach for novelty detection in multi-class scenarios with missing labels, demonstrating superior performance over existing methods.

Findings

Outperforms one-class SVM, k-NN, and KNFST methods

Effective on large multi-class image datasets

Significant improvement in novelty detection accuracy

Abstract

We address the problem of novelty detection in multiclass scenarios where some class labels are missing from the training set. Our method is based on the initial assignment of confidence values, which measure the affinity between a new test point and each known class. We first compare the values of the two top elements in this vector of confidence values. In the heart of our method lies the training of an ensemble of classifiers, each trained to discriminate known from novel classes based on some partition of the training data into presumed-known and presumednovel classes. Our final novelty score is derived from the output of this ensemble of classifiers. We evaluated our method on two datasets of images containing a relatively large number of classes - the Caltech-256 and Cifar-100 datasets. We compared our method to 3 alternative methods which represent commonly used approaches, including the one-class SVM, novelty based on k-NN, novelty based on maximal confidence, and the recent KNFST method. The results show a very clear and marked advantage for our method over all alternative methods, in an experimental setup where class labels are missing during training.