K-nearest neighbor

Peterson, Leif E.

doi:10.4249/scholarpedia.1883

Cited by 1,712 publications

(675 citation statements)

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“…k-Nearest Neighbors (kNN): kNN classifier is used to perform discriminant analysis when reliable parametric estimates of probability densities are unknown or difficult to determine [5]. K value is a positive integer, typically small, but normally larger than one.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Detecting Attacks in CyberManufacturing Systems: Additive Manufacturing Example

Zhou

Lin

et al. 2017

MATEC Web Conf.

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Abstract. CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms -(i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection -have been adopted in the research and shown to be effective in detecting such defects.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Detecting Attacks in CyberManufacturing Systems: Additive Manufacturing Example

Zhou

Lin

et al. 2017

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…K-nn is one of the techniques used for classification problems, and it is one of the most simple classification techniques that should be the first option for a classification study when there is no past knowledge about data description [13]. K-nn works first by computing distance between an instance with other instances and find the k-nearest neighbor for that instance, then it estimates the effort [3], as shown in fig.1.…”

Section: Machine Learning Techniques Usedmentioning

confidence: 99%

Software Development Effort Estimation Using Ensemble Machine Learning

2017

IJCCIE

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Abstract-In software engineering, the main aim is to develop a high quality project that fall within scheduled time and budget, this procedure is called effort estimation. Effort estimation is crucial and important for a company to do because hiring more people than needed will lead to loss of income, and hiring less people than needed will lead to delay of project delivery. The aim of this study is to estimate software effort objectively by using machine learning techniques instead of subjective and time consuming estimation methods. Models using two machine learning techniques which are Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN) separately and combining those together using ensemble learning were tried on two public datasets namely Desharnais and Maxwell. Results show that svm technique outperform k-nn technique, also ensemble learning improves the results.

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“…For the classification, the Euclidean distance is calculated between the new instance and the stated training samples [5]. KNN editing has the endowment of removing noisy instances from the training set [6].…”

Section: K-nearest Neighboursmentioning

confidence: 99%