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This article belongs to the Special Issue Advances in Quantum Artificial Intelligence and Machine Learning ; [Abstract] In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. In this context, a specific use case was built: an inferential network for testing the behaviour of the certainty factors approach in a quantum environment. After the design and execution of the experiments, the corresponding analysis of the obtained results was performed in three different scenarios: (1) inaccuracy in declarative knowledge, or imprecision, (2) inaccuracy in procedural knowledge, or uncertainty, and (3) inaccuracy in both declarative and procedural knowledge. This paper, as stated in the conclusions, is intended to pave the way for future quantum implementations of well-established methods for handling inaccurate knowledge. ; This work was supported by the European Union's Horizon 2020 research and innovation programme under project NEASQC (grant agreement No. 951821) and by the Xunta de Galicia (grant ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014–2020 Program, grant ED431G 2019/01) ; Xunta de Galicia; ED431C 2018/34 ; Xunta de Galicia; ED431G 2019/01

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021 ; [Abstract] At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases. ; This work has been supported by the State Research Agency of the Spanish Government, grant (PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia, grant (ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01 ; Xunta de Galicia; ED431C 2018/34 ; Xunta de Galicia; ED431G 2019/01

[Abstract] Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This paper is focused on implementing two approaches to this topic—Iterative Machine Teaching (iMT) and Active Learning (AL)—and analyzing how to integrate them in the learning loop. iMT is a variation of Machine Teaching in which a machine acts as a teacher that tries to transfer knowledge to a machine learning model. The focus of the problem in iMT is how to obtain the optimal training set given a machine learning algorithm and a target model. The idea is to learn a target concept with a minimal number of iterations with the smallest dataset. Active Learning, in contrast, is a specialized type of supervised learning in which humans are incorporated in the loop to act as oracles that analyze unlabeled data. AL allows us to achieve greater accuracy with less data and less training. Our proposal to incorporate iMT and AL into the machine learning loop is to use iMT as a technique to obtain the "Minimum Viable Data (MVD)" for training a learning model, that is, a dataset that allows us to increase speed and reduce complexity in the learning process by allowing to build early prototypes. Next, we will use AL to refine this first prototype by adding new data in an iterative and incremental way. We carried out several experiments to test the feasibility of our proposed approach. They show that the algorithms trained with the teachers converge faster than those that have been trained in a conventional way. Also, AL helps the model to avoid getting stuck and to keep improving after the first few iterations. The two approaches investigated in this paper can be considered complementary, as they correspond to different stages in the learning process. ; This work has been supported by the State Research Agency of the Spanish Government (grant PID2019-107194GB-I00 / AEI / 10.13039/501100011033) and by the Xunta de Galicia (grant ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigaci ́on de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program, grant ED431G 2019/01). ; Xunta de Galicia; ED431C 2018/34 ; Xunta de Galicia; ED431G 2019/01