Various Approaches to Modelling of the Mass Using the Size of the Class in the Centroid Based Classification

Rybak, Łukasz

doi:10.15199/13.2019.6.13

Cited by 2 publications

(5 citation statements)

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“…• n-Mass Model (n-MM) [19], which determines the value of data particles masses based on a size of classes; • Stochastic Learning Algorithm (SLA) [18], in which: maximum number of iterations maxIters = 50; coefficient of the mass value update ξ = 0.0001; expected error threshold ε = 0.00;…”

Section: Resultsmentioning

confidence: 99%

“…• mass m, which is expressed by a scalar quantity determined through applying one of the published approaches: Stochastic Learning Algorithm [18], Bath-update Algorithm [18], or n-Mass Model [19]; • centroid expressed by the vector µ with length |µ| = n, which defines the position of data particle in a feature space.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, in comparison, the approach of creating data particle based on a single data set element, 1CT1P significantly reduces the number of elements that will be processed in the next operations. Each of the mentioned approaches were combined with basic Nearest Centroid algorithm (NC) and three algorithms defining the data particle mass-n-Mass Model (n-MM) [19], Stochastic Learning Algorithm (SLA), and Bath-update Algorithm (BLA) [18]. In the experiments, k-fold cross validation method with k = 10 was applied and the obtained findings were expressed as precision, recall, and F-measure values.…”

Section: Introductionmentioning

confidence: 99%

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Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

Rybak¹,

Dudczyk²

2021

Applied Sciences

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The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their geometrical divide. In the previous analyses it was established that the Geometrical Divide (GD) method outperforms the algorithm creating the data particles based on classes by a compound of 1 ÷ 1 cardinality. This occurs in the process of balanced data sets classification, in which class centroids are close to each other and the groups of objects, described by different labels, overlap. The purpose of the article was to examine the efficiency of the Geometrical Divide method in the unbalanced data sets classification, by the example of real case-occupancy detecting. In addition, in the paper, the concept of the Unequal Geometrical Divide (UGD) was developed. The evaluation of approaches was conducted on 26 unbalanced data sets-16 with the features of Moons and Circles data sets and 10 created based on real occupancy data set. In the experiment, the GD method and its unbalanced variant (UGD) as well as the 1CT1P approach, were compared. Each method was combined with three data particle mass determination algorithms-n-Mass Model (n-MM), Stochastic Learning Algorithm (SLA) and Bath-update Algorithm (BLA). k-fold cross validation method, precision, recall, F-measure, and number of used data particles were applied in the evaluation process. Obtained results showed that the methods based on geometrical divide outperform the 1CT1P approach in the imbalanced data sets classification. The article’s conclusion describes the observations and indicates the potential directions of further research and development of methods, which concern creating the data particle through its geometrical divide.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

Rybak¹,

Dudczyk²

2021

Applied Sciences

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“…The authors of the presented algorithms indicated that the direction of further research may be to proofread the classifier, in which the mass of the data particle will depend on the numerical amount of the class. Therefore, in 2019, the effectiveness of the approach was put to the test, in which the mass value of a data particle results from the size of a particular class [ 18 ]. At that time, the aim of the research was to find out how the mass function influences the effectiveness of the Centroid-Based Classifier, which applies Newton’s Law of Universal Gravity [ 2 ].…”

Section: Methodsmentioning

confidence: 99%

“…The Maximum Distance Principium (MDP) algorithm creates a data particle based on the distance within the elements with the same label [5]. The approach creates a data particle on the basis of the object class with a 1 ÷ 1 compound (1CT1P), finding application, among others, in [17,18]. A number of approaches can be distinguished in the literature that focus on the determination of the centroid, e.g., [19,20], as mentioned in [17], and several approaches used to determine the mass of a data particle [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Geometrical Divide of Data Particle in Gravitational Classification of Moons and Circles Data Sets

Rybak¹,

Dudczyk²

2020

Entropy

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Thus far, the Universal Law of Gravitation has found application in many issues related to pattern classification. Its popularity results from its clear theoretical foundations and the competitive effectiveness of the classifiers based on it. Both Moons and Circles data sets constitute distinctive types of data sets that can be found in machine learning. Despite the fact that they have not been formally defined yet, on the basis of their visualization, they can be defined as sets in which the distribution of objects of individual classes creates shapes similar to circles or semicircles. This article makes an attempt to improve the gravitational classifier that creates a data particle based on the class. The aim was to compare the effectiveness of the developed Geometrical Divide method with the popular method of creating a class-based data particle, which is described by a compound of 1 ÷ 1 cardinality in the Moons and Circles data sets classification process. The research made use of eight artificially generated data sets, which contained classes that were explicitly separated from each other as well as data sets with objects of different classes that did overlap each other. Within the limits of the conducted experiments, the Geometrical Divide method was combined with several algorithms for determining the mass of a data particle. The research did also use the k-Fold Cross-Validation. The results clearly showed that the proposed method is an efficient approach in the Moons and Circles data sets classification process. The conclusion section of the article elaborates on the identified advantages and disadvantages of the method as well as the possibilities of further research and development.

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Various Approaches to Modelling of the Mass Using the Size of the Class in the Centroid Based Classification

Cited by 2 publications

References 0 publications

Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

A Geometrical Divide of Data Particle in Gravitational Classification of Moons and Circles Data Sets

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