An Empirical Analysis of Rough Set Categorical Clustering Techniques

Uddin, Jamal; Ghazali, Rozaida; Deris, Mustafa Mat

doi:10.1371/journal.pone.0164803

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…In the next stage, training data are presented to Gaussian naive Bayes (NB), decision tree (DT), K-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) algorithms. These learning algorithms were obtained from Scikit-learn library [25][26][27][28][29]. They are then compared according to the classification and regression performance metrics, which are explained in the ML section, while the most suitable algorithms are selected for positioning and localization.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Seçkin

Coşkun

2019

Applied Sciences

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Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude.

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Section: Methodsmentioning

confidence: 99%

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Seçkin

Coşkun

2019

Applied Sciences

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“…Therefore, energy efficiency has been a very popular keyword in WSN literature. Researchers have been trying to tackle this issue by different means such as proposing methods that directly focus on energy conservation such as sleep scheduling [19], or by proposing energy efficient algorithms such as energy efficient routing, energy efficient medium access control protocol, using clustering [20], and even energy efficient coverage enhancement specially in mobile sensors case [21,22], which perform their classical task more energy efficiently. For example, as is discussed in Reference [19], by carefully selecting a certain number of nodes to be activated to cover a desired portion of a monitored area, network life-time can improve more than 80% in certain areas.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

et al. 2019

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Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count.

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“…It is well known that the rough set theory was provided as a way to study the incomplete and unsure knowledge, and the method can simplify the calculations [ 21 – 22 ]. A comprehensive measurement method is proposed according to the rough set theory which based on some relative national standards and statistical data.…”

Section: Measurement Model Of Safety Operationmentioning

confidence: 99%

“…The rough set theory has been proved its effectiveness on machine learning, intelligent systems, inductive reasoning, decision analysis, and expert systems [ 21 ]. In especial it provides better performance in selecting the clustering attribute in terms of purity, entropy, accuracy and others [ 22 ]. In order to increase the objectivity of decision-making process, this paper adopts the fuzzy-distance method [ 23 ] to determine the weight coefficient of measurement index of high-speed railway safety operation.…”

Section: Introductionmentioning

confidence: 99%

A rough set-based measurement model study on high-speed railway safety operation

Tan

et al. 2018

PLoS ONE

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Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness.

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An Empirical Analysis of Rough Set Categorical Clustering Techniques

Cited by 9 publications

References 23 publications

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

A rough set-based measurement model study on high-speed railway safety operation

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