A parameter-free KNN for rating prediction

Fopa, Medjeu; M, Gueye; Ndiaye, Samba; Naacke, Hubert

doi:10.1016/j.datak.2022.102095

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…In scenarios with small-scale datasets where the distribution of signal data is irregular in space, it may be challenging for SVM to give a decision plane. In such cases, KNN [ 127 ], as an instance-based learning method, might offer a more straightforward solution. KNN classifies or regresses based on the majority class of the k-nearest neighbors around a new data point, making it less influenced by data distribution.…”

Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid–solid TENG and liquid–solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

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Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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“…Several similar studies have been carried out, such as in [14] which utilizes a Mini-PC (Raspberry Pi 3) [27], [28] to retrieve data from OBD-II which will be processed using Machine Learning [29], [30] with K-Nearest Neighbour (KNN) [31]- [33] dan Naive Bayesian methods [34]- [36].…”

Section: Introductionmentioning

confidence: 99%

Motorized Vehicle Diagnosis Design Using the Internet of Things Concept with the Help of Tsukamoto's Fuzzy Logic Algorithm

Nathanael Juwono,

Don Bosco Julienne,

Samuel Yogatama

et al. 2023

Journal of Robotics and Control

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There are many popular branches, including the Internet of Things (IoT) and Artificial Intelligence (AI), which have solved many problems. Same as that, the automotive field is also growing with the technology of OBD-II. Unfortunately, not many people are familiar with OBD-II even though the features offered are very varied to prevent vehicle damage. This proposed work uses an IoT and AI system to make a vehicle diagnosis system with a help of OBD-II technology. By using ESP32 to collect data in each vehicle and using one Mini-PC to run the diagnosis with Fuzzy Logic Tsukamoto for three or more vehicles, this work can decrease the research cost. This work also uses the Fuzzy Logic Tsukamoto to diagnose vehicle health which is considered very suitable in real-time data situations. The method that we proposed is using Iterative Waterfall because of its simplicity and because there is a feedback path in every step. Iterative Waterfall is divided into 4 stages, Requirement Gathering and Analysis, System Design, implementation of Development, and Testing. Numerical validation is included by using MAPE for the testing in the IoT system and AI system. According to the MAPE result for the IoT system, the engine off voltage is 0.9510789847% and the engine start voltage is 3.136217503% which is considered a very good result. The MAPE result for the AI system is quite high, which is 20.74364412%, and because of that, the AI system needed more research for better performance. Overall, the system that has been proposed is already successful in monitoring vehicle health based on the parameters that have been determined.

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“…Junior Medjeu Fopa et al propose the parameter-free KNN method for rating prediction called freeKNN. It dynamically selected an appropriate number of neighbors depending on the user and the item to be rated [3]. Some researchers propose an improved -nearest neighbor algorithm denoted as Dk-NN, using dynamic instead of a single value of [4], and some researchers varying from 1, 3, 5, …, √ and use the majority voting of all classes to label an instance [2].…”

Section: Introductionmentioning

confidence: 99%

Parameter-Free Conglomerate nearest Neighbor Classifier Using Mass-Ratio-Variance Outlier Factors

Fuangfoo,

Sinapiromsaran

2023

IJML

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Classification is one important area in machine learning that labels the class of an instance via a classifier from known-class historical data. One of the popular classifiers is k-NN, which stands for “k-nearest neighbor” and requires a global parameter k to proceed. This global parameter may not be suitable for all instances. Naturally, each instance may situate on different regions of clusters such as an interior instance placed inside a cluster, a border instance placed on the outskirts, an outer instance placed faraway from any cluster, which requires a different number of neighbors. To automatically assign a different number of neighbors to each instance, the concept of scoring from the anomaly detection research is desired. The Mass-ratio-variance Outlier Factor, MOF, is selected as the scoring scheme for the number of neighbors of each instance. MOF gives the highest score to an instance placed very far from any cluster and the lowest score to an instance surrounded by other instances. This leads to the proposed classifier called the conglomerate nearest neighbor classifier, which does not require any parameter assigning the appropriate number of neighbors to each instance ordered by MOF. Experimental results show that this classifier exhibits similar accuracy to the k-nearest neighbor algorithm with the best k over the synthesized datasets. Six UCI datasets, the QSAR dataset, the German dataset, the Cancer dataset, the Wholesale dataset, the Haberman dataset, and the Glass3 dataset are used in the experiment. This method outperforms two UCI datasets, Wholesale and Glass3, and displays similar performance with respect to these six UCI datasets.

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A parameter-free KNN for rating prediction

Cited by 13 publications

References 10 publications

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Motorized Vehicle Diagnosis Design Using the Internet of Things Concept with the Help of Tsukamoto's Fuzzy Logic Algorithm

Parameter-Free Conglomerate nearest Neighbor Classifier Using Mass-Ratio-Variance Outlier Factors

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