Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests

The vast amounts of data collected in various domains pose great challenges to modern data exploration and analysis. To find "interesting" objects in large databases, users typically define a query using positive and negative example objects and train a classification model to identify the objects of interest in the entire data catalog. However, this approach requires a scan of all the data to apply the classification model to each instance in the data catalog, making this method prohibitively expensive to be employed in large-scale databases serving many users and queries interactively. In this work, we propose a novel framework for such search-by-classification scenarios that allows users to interactively search for target objects by specifying queries through a small set of positive and negative examples. Unlike previous approaches, our framework can rapidly answer such queries at low cost without scanning the entire database. Our framework is based on an index-aware construction scheme for decision trees and random forests that transforms the inference phase of these classification models into a set of range queries, which in turn can be efficiently executed by leveraging multidimensional indexing structures. Our experiments show that queries over large data catalogs with hundreds of millions of objects can be processed in a few seconds using a single server, compared to hours needed by classical scanning-based approaches

End-to-End Neural Network Training for Hyperbox-Based Classification

Hyperbox-based classification has been seen as a promising technique in which decisions on the data are represented as a series of orthogonal, multidimensional boxes (i.e., hyperboxes) that are often interpretable and human-readable. However, existing methods are no longer capable of efficiently handling the increasing volume of data many application domains face nowadays. We address this gap by proposing a novel, fully differentiable framework for hyperbox-based classification via neural networks. In contrast to previous work, our hyperbox models can be efficiently trained in an end-to-end fashion, which leads to significantly reduced training times and superior classification results.Comment: 6 pages, accepted for poster presentation at ESANN 202

Artificial Intelligence in Education

This book contains a collection of 19 systematic literature reviews conducted by Cognitive Science students in the "Artificial Intelligence in Education" seminar in the winter term of 2021/2022. From a Cognitive Science and Artificial Intelligence (AI) perspective, the book investigates the state of the art of research on applying AI technology in educational settings as well as the strengths, weaknesses, opportunities, and threats of these applications. The four larger areas covered by the studies are "Didactics and Ethics", "Methods and Technologies", "Extended Reality and Robots", and "Addressing Special Needs"

Artificial Intelligence in Public Discourse

This book contains 26 studies conducted by students in the Cognitive Science seminar "Artificial Intelligence in Public Discourse". In their studies, they explore the use of the term Artificial Intelligence (AI) and related subfields in various parts of public discourse such as Twitter, user comments on news sites, expert interviews, government documents, television shows, newspapers, etc. It is investigated which strengths, weaknesses, opportunities, and threats are ascribed to AI technology and how this relates to the technical and academic state of the art and discussion. Most studies employ qualitative methods, but quantitative and mixed-methods approaches are also used