Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set

Raikwal, Jagdish; Saxena, Kanak

doi:10.5120/7842-1055

Cited by 67 publications

(51 citation statements)

References 12 publications

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“…Las aplicaciones científicas y de investigación son innumerables y la mayoría se han inclinado hacia el área de la mecatrónica con sus derivadas (Hayward, 2014). El presente trabajo consiste en crear una aplicación para dicho dispositivo en el campo de la inteligencia artificial, puntualmente en la visión por computadora, para realizar las cinco fases del procesamiento de imágenes (Szeliski,211) En el proceso de adquisición de imagen se disciernen dos etapas: la primera se conoce como captura y consiste en utilizar un dispositivo óptico para obtener información relativa a una escena o entorno deseado; la segunda se conoce como digitalización, y consiste en transformar una señal con uno o varios componentes continuos en una imagen digital (Raikwal & Saxena, 2012). La siguiente fase es conocida como segmentación, consiste en aislar las áreas de interés de una escena para interpretarlas.…”

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“…La investigación de trabajos previos (Raikwal & Saxena, 2012), (Sudha & Bhavani, 2012), (Lojo, Losada, & Barreiro, 2009), (Gutiérrez, Lastra, Bacardit, Benítez, & Herrera, 2016) llevó a la elección del clasificador de aprendizaje supervisado k-vecinos más cercanos (kNN) por su procesamiento, rendimiento, precisión, uso de memoria RAM, tiempo de respuesta, tiempo de búsqueda y entrenamiento del algoritmo.…”

Section: Introductionunclassified

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Hacia la construcción de un dispositivo de asistencia para personas no videntes en el juego de cuarenta

2017

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El presente trabajo tiene como objetivo el desarrollo de un sistema de reconocimiento automático de una carta de baraja ubicada sobre una mesa, como parte del proyecto más general de crear un dispositivo para asistir a personas no videntes en el juego de azar denominado “cuarenta”. El objeto de dicho dispositivo será informar al usuario de las cartas en juego, vía audio. Para esta fase del proyecto se utilizó el algoritmo de k-vecinos más cercanos entrenado con un conjunto de caracteres alfanuméricos sintéticos. El conjunto de prueba constó de fotografías tomadas en condiciones controladas de iluminación, con las cartas ubicadas en orientación arbitraria. La parametrización del algoritmo arrojó un valor de 1 como k óptimo, con el cual se obtuvo un error de clasificación en el conjunto de prueba de 5%. Solo dos caracteres fueron confundidos por el clasificador, la “A” y la “J”, con errores de 20% y 40% cada uno. El algoritmo fue implementado en un sistema embebido Raspberry Pi 3, obteniendo un tiempo de respuesta de 5 segundos, incluida la conversión a audio, y una ocupación de memoria RAM que no superó el 60% de la capacidad del sistema. Estos resultados sugieren su aplicabilidad en dispositivos portátiles.

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Section: Introductionunclassified

Hacia la construcción de un dispositivo de asistencia para personas no videntes en el juego de cuarenta

2017

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“…(KNN) is one of the effective, simple and performs well method for classification [1][2][3], but from several studies, the result performance accuracy of KNN is lower than other methods. One of them is in the study by [4] which compared performance between support vector machine (SVM) and KNN. The result of their research seen that performance of SVM better than KNN, where the value accuracy obtained by SVM of 82.54% while the value obtained from KNN of 79.22%.…”

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Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Nababan¹,

Sitompul

Tulus

2018

J. Phys.: Conf. Ser.

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Abstract. K-Nearest Neighbor (KNN) is a good classifier, but fro m several studies, the result performance accuracy of KNN still lo wer than other methods. One of the causes of the low accuracy produced, because each attribute has the same effect on the classification process, while some less relevant characteristics lead to mis s-classification of the class assignment for new data. In this research, we proposedAttribute Weighting Based K-Nearest Neighbor Using Gain Rat io as a parameter to see the correlation between each attribute in the data and the Gain Ratio also will be used as the basis for weighting each attribute of the dataset. The accuracy of results is compared to the accuracy acquired fro m the original KNN method using 10-fo ld Cross-Validation with several datasets from the UCI Machine Learning repository and KEELDataset Repository, such as abalone, glass identification,haberman, hayes -roth and water quality status. Based on the result of the test, the proposed method was able to increase the classification accuracy of KNN, where the highest difference of accuracy obtained hayes-roth dataset is worth 12.73%, and the lowest difference of accuracy obtained in the abalone dataset of 0.07%. The average result of the accuracy of all dataset increases the accuracy by 5.33%. (KNN) is one of the effective, simple and performs well method for classification [1][2][3], but from several studies, the result performance accuracy of KNN is lower than other methods. One of them is in the study by [4] which compared performance between support vector machine (SVM) and KNN. The result of their research seen that performance of SVM better than KNN, where the value accuracy obtained by SVM of 82.54% while the value obtained from KNN of 79.22%. Another study by [5] compared K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The results of their study seen that the performance of ANN (with 5 hidden layers) better than KNN, where the result of accuracy is 90.5%. Research by [6] compared the performance of Naïve Bayes, Decision Tree and K-Nearest Neighbor (KNN). The results show that Naïve Bayes has the best accuracy in classification compared to Decision Tree Tree and K-Nearest Neighbor (KNN) with an average accuracy of 73.7%. whilethe average accuracy of Decision Tree and KNN respectively 58.9% and 56.7%. In the study by [7] compared KNN and Naïve Bayes in diagnosing heart disease. Results obtained by the value of accuracy Naïve Bayes of 79.62% while the average value of accuracy on KNN is only 64.85% whenk = 10. Introduction K-Nearest Neighbor

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“…Therefore approaches dealing with the creation of the classifiers' ensembles for the accuracy increase of the classification solution have been investigated [3][4][5]. The training of the SVM ensemble is the training procedure of the finite set of the base (individual) classifiers: the individual solutions are combined to form the resulting classification decisions, based on the aggregated classifier.…”

Section: Introductionmentioning

confidence: 99%

“…The SVM classifiers based on the SVM algorithm have been applied for credit risk analysis [3], medical diagnostics [4], handwritten character recognition [5], text categorization [6], information extraction [7], pedestrian detection [8], face detection [9], Earth remote sensing [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles

Demidova¹,

Nikulchev²,

Sokolova³

2016

ijacsa

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Abstract-The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the highprecision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

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Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set

Cited by 67 publications

References 12 publications

Hacia la construcción de un dispositivo de asistencia para personas no videntes en el juego de cuarenta

Hacia la construcción de un dispositivo de asistencia para personas no videntes en el juego de cuarenta

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles

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