Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning

<p>Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the proposed interactive and lossless multidimensional visualization, end users can identify the pattern in the data based on which they can make explainable decisions. Such options would not be possible in black box machine learning methodologies. The interpretable IVLC algorithm is supported by the Interactive Shifted Paired Coordinates Software System (SPCVis). It is a lossless multidimensional data visualization system with interactive features. The interactive approach provides flexibility to the end user to perform data classification as self-service without having to rely on a machine learning expert. iv Interactive pattern discovery becomes challenging while dealing with large datasets with hundreds of dimensions/features. To overcome this problem, an automated classification approach combined with new Coordinate Order Optimizer (COO) algorithm and a Genetic algorithm (GA) is proposed. The COO algorithm automatically generates the coordinate pair sequences that best represent the data separation and GA helps optimizing the proposed IVLC algorithm by automatically generating the areas for data classification. The feasibility of the approach is shown by experiments on benchmark datasets covering both interactive and automated processes used for data classification.</p>

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2021

Author dc:creator

• Wagle, Sridevi Narayana

Contributors dc:contributor

• Boris Kovalerchuk
• Razvan Andonie
• Szilard Vajda

• Shifted Paired Coordinates
• Interactive Data Visualization
• Democratized Machine Learning
• Iterative Visual Logical Classifier
• Coordinate Order Optimizer
• Genetic Algorithm.
• Computational Engineering
• Data Science
• Other Computer Sciences

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/1503

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-2528