Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset Description An account of the resource The proposed chapter deals with psychological data related to depression, anxiety, and stress to study how the classification and analysis is carried out on imbalanced data. The proposed study not only contributes on providing practical information about the balancing techniques such as synthetic minority oversampling technique but also reveals the strategy for dealing with the working of many existing classification algorithms such as the support vector machine, random forest, XGBoost, etc. on the imbalanced dataset. The present use of evaluation metrics that are solely implied for the imbalanced data classification is also illustrated. It was observed that the ordinary model assessment techniques do not precisely quantify model execution when gone up against imbalanced datasets and that the common techniques such as the logistic regression and decision tree have a predisposition toward classes that have many observations. The attributes of the minority class are treated low and are routinely overlooked. Henceforth, there is a high likelihood of misclassification of the minority class when compared to the majority class. A confusion matrix which contains data about the real and predicted class is used as an assessment standard to check the exhibition of grouping calculation. Rather than going for accuracy, F-score and the area under the curve are considered as the measures to evaluate the classification model. 2022 selection and editorial matter, Vishal Jain, Sapna Juneja, Abhinav Juneja, and Ramani Kannan. Creator An entity primarily responsible for making the resource Umme Salma M.; Amala Ann K.A. Source A related resource from which the described resource is derived Handbook of Machine Learning for Computational Optimization: Applications and Case Studies, pp. 251-266. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003138020-14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003138020-14</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132463979&amp;doi=10.1201%2F9781003138020-14&amp;partnerID=40&amp;md5=d18c38d6e87f0ca6f9fd2cc34fdfff90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132463979&amp;doi=10.1201%2f9781003138020-14&amp;partnerID=40&amp;md5=d18c38d6e87f0ca6f9fd2cc34fdfff90</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100045567-0; 978-036768542-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Umme Salma M., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Amala Ann K.A., Data Science Department, CHRIST (Deemed to be University), Bangalore, India