U.S. Department of Energy

Pacific Northwest National Laboratory

Interactive Machine Learning at Scale with CHISSL

Publish Date: 
Wednesday, February 7, 2018
We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capable of incorporating user feedback incrementally and immediately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deterministically, with O(n) space and time complexity. Our algorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.
Arendt D.L., E.A. Grace, and S. Volkova. 2018. "Interactive Machine Learning at Scale with CHISSL." In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), February 2-7, 2018, New Orleans, Louisiana, 8194-8195. Palo Alto, California:Association for the Advancement of Artificial Intelligence. PNNL-SA-129748.
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