Scalable Machine Learning with Granulated Data Summaries: A Case of Feature Selection

Chądzyńska-Krasowska, Agnieszka; Betliński, Paweł; Ślęzak, Dominik

doi:10.1007/978-3-319-60438-1_51

Cited by 6 publications

(1 citation statement)

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“…The last aspect is the analysis in a wider time window that captures trends and tendencies arising from the repetition of certain phenomena in a defined historical window. Calculating approximated results based on meta-descriptions and summaries are widely applied in many areas of interest, such as analytical databases [13], large relational data sets [12], redesigning and accelerating machine learning algorithms [2] or systems for monitoring health conditions for members of nursing homes [5]. A slightly different approach is to use Japanese candles as summaries [9], and then compute and process that data.…”

Section: B Goal Descriptionmentioning

confidence: 99%

Generating Fuzzy Linguistic Summaries for Menstrual Cycles

Sosnowski

Penza²

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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This paper presents a method of generating linguistic summaries of women's menstrual cycles based on the set of concepts describing various aspects of the cycles. These concepts enable description of menstrual cycles that are readable for humans, but they also provide high-level information that can be used as control input for other data processing actions such as e.g. anomaly detection. The labels signifying these concepts are assigned to cycles by means of multivariate time series analysis. The corresponding algorithm is a subsystem of a bigger solution created as a part of an R&D project.

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