Deep Ensemble Outlier Detection in Lymphography Dataset | Chapter 5 | Contemporary Perspective on Science, Technology and Research Vol. 1

 Main data instances famous as outliers are those whose characteristics differ from those of the most of the instances in a dataset. With a difference of uses, including deception detection in credit card undertakings and intrusion discovery in computer route, outlier detection is a critical field of study in statistics and dossier mining. In the healing field, diseases can also be recognized from a variety of lab reports using aberration detection techniques. Investigators have developed any of techniques to recognize outliers in healthcare systems. This research aims to identify the outliers utilizing Deep Ensemble Approach in lymphography dataset. In order to recognize outliers in the lymphography dataset, this research suggests an ensemble approach established deep learning and isolation thickets. The suggested approach outperforms the individual models, in accordance with experimental results utilizing the lymphography dataset from the UCI machine learning repository that is to say accessible to the society.

Author(s) Details:

J. E. Judith,
Noorul Islam Centre for Higher Education, Kumaracoil, India.

Roy Thomas,
Noorul Islam Centre for Higher Education, Kumaracoil, India.

C. Dhayananth Jegan,
Stella Mary’s College of Engineering, Nagercoil, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V1/article/view/12690