Interactive Machine Learning focuses on human-centered aspects of Machine Learning and the iterative machine learning process. Overall goal is to train powerful machine learning models by combining both the strengths of humans and machines.
Our research in Interactive Machine Learning is based on our expertise in the design of interactive visual interfaces on the one hand and by leveraging non-visual data analysis capability on the other hand. Particular research interests on concepts, techniques, and applications are along the following methodologies and research branches:
Members of IVDA have published more than 50 research papers, all of which addressing Interactive Machine Learning capability. The following hub website provides an overview of Interactive Machine Learning papers.
Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. In this project, we are designing and developing interactive visualization approaches to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of our approaches with several usage scenarios, using datasets posted at our project page.
Papers and Datasets: