Date of Award

5-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering and Computer Science

First Advisor

Marco M. Carvalho

Second Advisor

Hector M. Gutierrez

Third Advisor

Thomas Eskridge

Fourth Advisor

Munevver Subasi

Abstract

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In OSR, only a limited number of known classes are available at the time of training the model and the possibility of unknown classes never seen at training time emerges in the test environment. In such a setting, the unknown classes and their risk should be considered in the algorithm. Such systems require not only to identify and discriminate instances that belong to the source domain (i.e., the seen known classes contained in the training dataset) but also to reject unknown classes in the target domain (classes used in the testing phase). Until recently, the success of almost all machine-learning-based systems has been obtained by conducting them on closed-set classification tasks. In such systems, the source and target domains are assumed to contain the same object classes and the system is only tested on known classes that have been seen during training. Different from the closed set setting, a more realistic scenario is solving real-world problems consisting of an open set of objects. In this dissertation, we propose, develop, and demonstrate an efficient algorithm to improve classification in Open Set Recognition tasks. The proposed technique will explore a new representation of feature space. The efficacy and efficiency of many applications can be improved by integrating OSR, which offers more precise and insightful predictions of outcomes. We demonstrate the performance of the proposed method on three established datasets. The results indicate that the proposed model outperforms the baseline methods in accuracy and F1-score.

Comments

Copyright held by author

Download

Share

COinS