k-Nearest Neighbor Classification

Mucherino, Antonio; Papajorgji, Petraq; Pardalos, Pãnos M.

doi:10.1007/978-0-387-88615-2_4

Cited by 226 publications

(99 citation statements)

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“…The RD classifier acts as a proxy for the behaviour of the researchers who do not know much about model checking tools, while SD can be considered as mirroring the behaviour of experienced verification researchers who know the patterns supported by each tool and the fastest tools distribution, but do not know which is the best tool for a specific property to be checked on a specific model . The remaining five methods are: SVM classifier (Chang and Lin, 2011); LR (Yu et al , 2011); KNN classifier (Mucherino et al , 2009); and two types of ensemble methods, namely, ERT (Geurts et al , 2006) and RFs (Breiman, 2001) (despite their names these are not random classifiers, but ensemble classifiers). We used the scikit-learn library (Pedregosa et al , 2011) implementation of these classifiers in our experiments.…”

Section: Resultsmentioning

confidence: 99%

“…We have compared seven methods; five of them are powerful and widely used algorithms, namely, support vector machine classifier (SVM) (Chang and Lin, 2011), logistic regression (LR) (Yu et al , 2011), K-nearest neighbour classifier (KNN) (Mucherino et al , 2009), extremely randomized trees (ERT) (Geurts et al 2006) and random forests (RFs) (Breiman, 2001) and two of the classifiers are for baseline predictions, namely, Random Dummy (RD) and Stratified Dummy (SD). We used 10-fold cross-validation for training and testing the classifiers.…”

Section: Methodsmentioning

confidence: 99%

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Automatic selection of verification tools for efficient analysis of biochemical models

et al. 2018

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Motivation Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest.ResultsWe provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the ‘usability barrier’ enabling biologists to have access to this powerful computational technology.Availability and implementationSMC Predictor tool is available at http://www.smcpredictor.com.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic selection of verification tools for efficient analysis of biochemical models

et al. 2018

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“…A two-stage approach is proposed here to identify the outliers. First of all, the SUT K-Nearest Neighbors [35] are extracted from the matrix F M−1 , obtaining K different signatures f k , again, stacked in a matrix F k .…”

Section: Intrusion Attack Detection Algorithmmentioning

confidence: 99%

Aircraft Mode S Transponder Fingerprinting for Intrusion Detection

Leonardi

Gerardi

2020

Aerospace

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Nowadays, aircraft safety is based on different systems and four of them share the same data-link protocol: Secondary Surveillance Radar, Automatic Dependent Surveillance System, Traffic Collision Avoidance System, and Traffic Information System use the Mode S protocol to send and receive information. This protocol does not provide any kind of authentication, making some of these applications vulnerable to cyberattacks. In this paper, an intrusion detection mechanism based on transmitter Radio Frequency (RF) fingerprinting is proposed to distinguish between legitimate messages and fake ones. The proposed transmitter signature is described and an intrusion detection algorithm is developed and evaluated in case of different intrusion configurations, also with the use of real recorded data. The results show that it is possible to detect the presence of fake messages with a high probability of detection and very low probability of false alarm.

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“…[17] used k-nearest neighbor classification techniques to classify the agricultural data using eulidean distance hamming distance and manhattan distance methods. Unda-Trillas, E. et, al [18] described the methods to build classification and regression trees(CART) which uses Gini Index as impurity index that is a generalization of bionomial variance. Berk, R.A et.…”

Section: Releated Workmentioning

confidence: 99%

Building Intelligent Conveyor System using classification techniques in a logistics Industry

2019

ijeat

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 Abstract: Machine learning techniques plays an important role in knowledge discovery and assists humans in decision making. They help to detect patterns and predict the actions/outcome. In a complex industrial environment mode of operations of a machine depends on various internal and external parameters which are often done using expert judgement method which is not accurate and results in machine breakdown thereby resulting in unplanned outage. In this paper, we discussed and demonstrated how machine learning algorithms can help to handle conveyor systems autonomously in an optimum way without any human intervention. A conveyor belt system operational data is used to select the appropriate classification technique for the selected dataset. The details of the dataset collected, algorithms used and the test results are discussed in this paper.

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k-Nearest Neighbor Classification

Cited by 226 publications

References 0 publications

Automatic selection of verification tools for efficient analysis of biochemical models

Automatic selection of verification tools for efficient analysis of biochemical models

Aircraft Mode S Transponder Fingerprinting for Intrusion Detection

Building Intelligent Conveyor System using classification techniques in a logistics Industry

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