With the development of learning algorithms, the constantly increasing computing power and the available amount of multimedia data, the adoption rate of data science techniques is steadily growing. Machine and deep learning algorithms are already used in a wide variety of ways to solve domain-specific problems. However, the potential of such methodologies will be fulfilled when also non specialized data scientists will be empowered with their use. Focusing on such perspective, this work does not deal with a classical data science problem, but instead exploits existing and available easy to use data-human interfaces. To this aim, we picked an exemplar scenario, amounting to an existing qualitative activity recognition data set that was in the past analyzed utilizing feature selection techniques and custom machine learning paradigms. We here verify how it is today possible, without changing the default settings and/or performing any type of feature selection, to employ the machine and deep learning algorithms provided by different publicly accessible tools (namely, Weka, Orange, Ludwig and KNIME) to address the same problem. Nevertheless, not all of the utilized platforms and algorithms provided satisfactorily results: we here finally discuss the possible issues and opportunities posed by such approach.

Using off-the-shelf data-human interface platforms: traps and tricks

Angeli A.;Marfia G.;
2021

Abstract

With the development of learning algorithms, the constantly increasing computing power and the available amount of multimedia data, the adoption rate of data science techniques is steadily growing. Machine and deep learning algorithms are already used in a wide variety of ways to solve domain-specific problems. However, the potential of such methodologies will be fulfilled when also non specialized data scientists will be empowered with their use. Focusing on such perspective, this work does not deal with a classical data science problem, but instead exploits existing and available easy to use data-human interfaces. To this aim, we picked an exemplar scenario, amounting to an existing qualitative activity recognition data set that was in the past analyzed utilizing feature selection techniques and custom machine learning paradigms. We here verify how it is today possible, without changing the default settings and/or performing any type of feature selection, to employ the machine and deep learning algorithms provided by different publicly accessible tools (namely, Weka, Orange, Ludwig and KNIME) to address the same problem. Nevertheless, not all of the utilized platforms and algorithms provided satisfactorily results: we here finally discuss the possible issues and opportunities posed by such approach.
2021
Angeli A.; Marfia G.; Riedel N.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/772645
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