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The Motion Analysis Blog

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Feature space reduction method for ultrahigh-dimensional, multiclass data: RFMS

Gergely Hanczár, Marcell Stippinger, Dávid Hanák, Marcell T. Kurbucz, Olivér Máté Törteli, Ágnes Chripkó & Zoltán Somogyvári

(The ones that are not Trade Secrets)

Hanczár, G., Stippinger, M., Törteli, O. M., Hanák, D., Chripko A., Somogyvári, Z. & Kurbucz, T. M. (2023). Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS). Machine Learning: Science and Technology, 4, 045012.


Hanczár, G., Griechisch, E., Ovád, N., Törteli, O. M., Tóth, G., Hanák, D., Vértes, B., Horváth, A., & Kamondi, A. (2022). Detection of mild cognitive impairment based on mouse movement data of trail making test. Informatics in Medicine Unlocked, 35, 101120.

Hanczár, G., Griechisch, E., Molnár, Á., & Tóth, G. (2022). Motor Movement Data Collection from Older People with Tablets and Digital Pen-Based Input Devices. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham.

Stippinger, M., Hanák, D., Kurbucz, M. T., Hanczár, G., Törteli, O. M., & Somogyvári, Z. (2023). BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space. SoftwareX, 22, 101366.

Griechisch, E., Ward, J. R., & Hanczár, G. (2019). Anomalies in measuring speed and other dynamic properties with touchscreens and tablets. 2019 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, pp. 1-6.

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