An efficient instance selection algorithm for k nearest neighbor regression

Song, Yunsheng; Liang, Jiye; Lü, Jing; Zhao, Xin

doi:10.1016/j.neucom.2017.04.018

Cited by 295 publications

(96 citation statements)

References 23 publications

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“…We introduced input selection by growing algorithm [36][37][38][39]. To find out the optimal number of artificial neurons, we used an order selection algorithm [40,41]. To evaluate the adopted approach and study the loss, we prepared a receiver operating characteristic (ROC) curve.…”

Section: Application and Analysismentioning

confidence: 99%

Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning

2019

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Concrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure. Appropriate quality and the preservation of operational parameters of machine foundations are essential, especially in the gas and oil industry, where every disruption in the technological process is costly. First and foremost, there are direct repair costs from damage to foundations, but there are also indirect costs associated with blockages of the production process. Foundation repairs can temporarily shut down a given part of the refining process from operation. Thanks to cooperation from our partner, we obtained data from 510 concrete machine foundations from a refinery. Our database included many parameters, such as concrete cover thickness, machine gravity center distortion, the angular frequency of vertical self-excited vibrations, the angular frequency of horizontal self-excited vibrations, amplitudes of oscillation, foundation area, foundation volume, and information on occurring failures. Concrete machine foundation failure is not yet fully understood. In our study, we assessed what affects the failure occurrence rate of concrete machine foundations and to what extent. We wanted to find out whether there are correlations between the foundation failure occurrence rate and the mentioned parameters. To achieve this goal, we utilized state-of-the-art machine learning techniques.

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Section: Application and Analysismentioning

confidence: 99%

Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning

2019

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“…LDA is a common supervised identification method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events [18]. -NN is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions) [19]. The calculation process is conducted in Matlab 7.11.0 R2010b (MathWorks, Natick, USA).…”

Section: Datamentioning

confidence: 99%

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Wei

et al. 2017

Journal of Food Quality

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Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of ( 670 + 760 − 640 )/( 670 + 760 + 640 ) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index (( 670 + 760 − 640 )/( 670 + 760 + 640 )). Different indexes were used as the input of linear discriminate analysis (LDA) and of -nearest neighbor ( -NN) algorithm to identify the maturity, and it was found that -NN with ( 670 + 760 − 640 )/( 670 + 760 + 640 ) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

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“…To this end, the literature provides a variety of approaches. There are instance selection methods deliberately designed for specific classifiers including K−nearest neighbors [ e.g., [11][12][13][14][15] and support vector machine [ e.g., [16][17][18]. Also, there are studies on more general methods mostly constructed on a prescribed distance metrics and applications [ e.g., [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Optimal training and test sets design for machine learning

Genç¹,

Tunç²

2019

Turk J Elec Eng & Comp Sci

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In this paper, we describe histogram matching, a metric for measuring the distance of two datasets with exactly the same features, and embed it into a mixed integer programming formulation to partition a dataset into fixed size training and test subsets. The partition is done such that the pairwise distances between the dataset and the subsets are minimized with respect to histogram matching. We then conduct a numerical study using a well-known machine learning dataset. We demonstrate that the training set constructed with our approach provides feature distributions almost the same as the whole dataset, whereas training sets constructed via random sampling end up with significant differences. We also show that our method introduces neither positive nor negative bias in prediction accuracy of a decision tree—used as a representative example of a machine learning method.

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An efficient instance selection algorithm for k nearest neighbor regression

Cited by 295 publications

References 23 publications

Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning

Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Optimal training and test sets design for machine learning

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