Optimization of distance formula in K-Nearest Neighbor method

Lubis, Arif Ridho; Lubis, Muharman; Al-Khowarizmi, Al-Khowarizmi

doi:10.11591/eei.v9i1.1464

Cited by 70 publications

(35 citation statements)

References 14 publications

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“…The KNN classification algorithm works on the basis of the Euclidean distance and calculates the probability of the test inputs that are in close proximity to the data points. 31 The results acquired in our study indicate lowest accuracies generated via LDA for both FAP and FHP conditions. LDA executes mathematical operation on the basis of probability estimation and estimates a new set of inputs that belong to each class.…”

Section: Discussionmentioning

confidence: 54%

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition

Ali

Khan

Azim

et al. 2021

Proc Inst Mech Eng H

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The utilization of surface EMG and intramuscular EMG signals has been observed to create significant improvement in pattern recognition approaches and myoelectric control. However, there is less data of different arm positions and hand postures available. Hand postures and arm positions tend to affect the combination of surface and intramuscular EMG signal acquisition in terms of classifier accuracy. Hence, this study aimed to find a robust classifier for two scenarios: (1) at fixed arm position (FAP) where classifiers classify different hand postures and (2) at fixed hand posture (FHP) where classifiers classify different arm positions. A total of 20 healthy male participants (30.62 ± 3.87 years old) were recruited for this study. They were asked to perform five motion classes including hand grasp, hand open, rest, hand extension, and hand flexion at four different arm positions at 0°, 45°, 90°, and 135°. SVM, KNN, and LDA classifier were deployed. Statistical analysis in the form of pairwise comparisons was carried out using SPSS. It is concluded that there is no significant difference among the three classifiers. SVM gave highest accuracy of 75.35% and 58.32% at FAP and FHP respectively for each motion classification. KNN yielded the highest accuracies of 69.11% and 79.04% when data was pooled and was classified at different arm positions and at different hand postures respectively. The results exhibited that there is no significant effect of changing arm position and hand posture on the classifier accuracy.

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Section: Discussionmentioning

confidence: 54%

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition

Ali

Khan

Azim

et al. 2021

Proc Inst Mech Eng H

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“…The formation of a cluster is based on the Euclidian formula. If nodes are in the range of Euclidian distance [69], then they become neighbors. These neighboring nodes are used in the cluster formation on the basis of density.…”

Section: K Aes-clustering Algorithmmentioning

confidence: 99%

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

et al. 2020

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Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks. INDEX TERMS Intelligent transport system, security, vehicular ad-hoc networks, trust-based clustering, VANET attacks.

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“…In obtaining a small error value, of course, a technique of measuring accuracy must be carried out as is done by Lubis [24] to analyse bank customer data and measure the accuracy value looking for the smallest error with MAPE and MSE so that in his research the smallest error value was achieved using MAPE. In addition, reference [25] also applies the smallest error value accuracy in the Simple Evolving Connectionist System method using MAPE but is influenced by the normalized distance formula.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Al-Khowarizmi

Syah

Nasution

et al. 2021

IJECE

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Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

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Optimization of distance formula in K-Nearest Neighbor method

Cited by 70 publications

References 14 publications

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition

StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

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