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11.01.2020 | Dissertation and Habilitation Abstracts

Dealing with Mislabeling via Interactive Machine Learning

verfasst von: Wanyi Zhang, Andrea Passerini, Fausto Giunchiglia

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 2/2020

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Abstract

We propose an interactive machine learning framework where the machine questions the user feedback when it realizes it is inconsistent with the knowledge previously accumulated. The key idea is that the machine uses its available knowledge to check the correctness of its own and the user labeling. The proposed architecture and algorithms run through a series of modes with progressively higher confidence and features a conflict resolution component. The proposed solution is tested in a project on university student life where the goal is to recognize tasks like user location and transportation mode from sensor data. The results highlight the unexpected extreme pervasiveness of annotation mistakes and the advantages provided by skeptical learning.

Vorheriger Artikel Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning

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In order to support its argument, the machine could provide some sort of explainable critique to the user feedback, in terms of counter-examples or evidence of inconsistencies with respect to the SK. This is a promising direction for future research.

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Titel: Dealing with Mislabeling via Interactive Machine Learning
verfasst von: Wanyi Zhang
Andrea Passerini
Fausto Giunchiglia
Publikationsdatum: 11.01.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: KI - Künstliche Intelligenz / Ausgabe 2/2020
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI: https://doi.org/10.1007/s13218-020-00630-5

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