LSTM Processing of Experimental Time Series with Varied Quality

Podlaski, Krzysztof; Durka, Michal; Gwizdałła, Tomasz M.; Miniak-Górecka, Alicja; Fortuniak, Krzysztof; Pawlak, Włodzimierz

doi:10.1007/978-3-030-77980-1_44

Cited by 3 publications

(3 citation statements)

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“…The method seems to be promising, and as the main directions of further development, we can enlist: the effect of the method on uncertainty reduction and the reconstruction of experimental data ( Podlaski et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, the results presented in the article show that the proposed method can produce results visibly better than SVM. The uncertainty problems are discussed in many papers, but the authors often use more sophisticated solutions like LSTM networks ( Karasu & Altan, 2022 ; Podlaski et al, 2021 ). Here we use the shallow learning approach that is much less demanding on the resources.…”

Section: Introductionmentioning

confidence: 99%

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Self-optimizing neural network in the classification of real valued data

Miniak-Górecka

Podlaski

Gwizdałła

2022

PeerJ Computer Science

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The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called self-optimizing neural networks (SONN). The proposed approach works on discretized data. Using a special procedure, we assign a feature vector to each element of the real-valued dataset. Later the feature vectors are analyzed, and decision patterns are created using so-called discriminants. We focus on how these discriminants are used and influence the final classifier prediction. Moreover, we also discuss the influence of the neighborhood topology. In the article, we use three different datasets with different properties. All results obtained by derived methods are compared with those obtained with the well-known support vector machine (SVM) approach. The results prove that the proposed solutions give better results than SVM. We can see that the information obtained from a training set is better generalized, and the final accuracy of the classifier is higher.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-optimizing neural network in the classification of real valued data

Miniak-Górecka

Podlaski

Gwizdałła

2022

PeerJ Computer Science

Self Cite

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“…Let us now check how it works for real data obtained in an experiment. The data considered were wind direction measurements conducted at Biebrza National Park's wetlands in northeastern Poland in May 2016 [44,45]. The wind direction is an angle deviation from the north direction and must be treated as periodic data.…”

Section: Clustering a Real Angular Datasetmentioning

confidence: 99%

Using K-Means Clustering in Python with Periodic Boundary Conditions

2022

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Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

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