Ranking Mutual Information Dependencies in a Summary-based Approximate Analytics Framework

Ślęzak, Dominik; Borkowski, Janusz; Chądzyńska-Krasowska, Agnieszka

doi:10.1109/hpcs.2018.00137

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In this section, the feature weighting performance of the ELM method is compared against those of one unsupervised feature weighting method: PCA [14] and two supervised feature weighting methods: MI [15] and BPNN, in terms of dimensionality reduction. Here, BPNN is implemented with the use of 100 hidden nodes, the sigmoid activation function, learning rate = 0.2 and the maximum iteration = 5000.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A Study of Neuro-Weighted Nearest-Neighbour Classification

Yue

Deng

2021

Advances in Intelligent Systems and Computing

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Irrelevant features of the dataset have a certain impact on the judgment ability of the classifier. Effectively distinguishing strong and weakly relevant features can improve classification performance. Since neural networks can effectively dig out the underlying impact of the conditional features on the decision, in this work, the knowledge learned and stored in the parameters of neural networks will be applied to weight the features. Furthermore, the weighted features will be used in the similarity-based nearest-neighbour (SNN) classifier to enhance the model's classification performance. The experimental results demonstrate that the proposed weighted nearest-neighbour approach generally outperforms many popular classification techniques.

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Section: Dimensionality Reductionmentioning

confidence: 99%

A Study of Neuro-Weighted Nearest-Neighbour Classification

Yue

Deng

2021

Advances in Intelligent Systems and Computing

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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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Toward automatic assessment of a risk of women’s health disorders based on ontology decision models and menstrual cycle analysis

Sosnowski

Wróblewski²

2021

2021 IEEE International Conference on Big Data (Big Data)

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Ranking Mutual Information Dependencies in a Summary-based Approximate Analytics Framework

Cited by 3 publications

References 20 publications

A Study of Neuro-Weighted Nearest-Neighbour Classification

A Study of Neuro-Weighted Nearest-Neighbour Classification

Generating Fuzzy Linguistic Summaries for Menstrual Cycles

Toward automatic assessment of a risk of women’s health disorders based on ontology decision models and menstrual cycle analysis

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