Design, Implementation and Comparative Study of Supervised Classification Algorithms for Object Sorting

Autor:	K P J Pradeep, C. Chandra Mouli, P. Jyothi, K. Nagabhushan Raju
Rok vydání:	2014
Předmět:	business.industry Computer science Classification procedure Pattern recognition computer.software_genre k-nearest neighbors algorithm Statistical classification Euclidean geometry Training phase Artificial intelligence Data mining business Classifier (UML) computer Sample image
Zdroj:	IOSR Journal of Electrical and Electronics Engineering. 9:51-60
ISSN:	2278-1676 2320-3331
DOI:	10.9790/1676-09445160
Popis:	The present study contributes the detailed design, Implementation and comparative studies of supervised classification algorithms such as Nearest Neighbor (NN),k-Nearest Neighbor (k-NN) and Minimum Mean Distance (MMD) along with the distance metrics Maximum, Sum (Manhattan) and Euclidean distances. Classification involves training and testing phases. Training phase teaches the classifier about the types of sample images to classify during the testing phase and saves the class names in a classifier file. Testing phase classifies an unknown sample image into one of the class from classifier file. Total numbers of sample images were divided into the ratio of 30:70 for training and testing respectively. Then classification procedure was performed on sample images for each algorithm with each distance metric. The training and testing VIs for particle and color classification were designed and implemented by using LabVIEW. All the algorithms and distance metrics were analyzed, compared for best results and the maximum accuracy algorithm along with its distance metric is going to implement in real time object sorting application.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7029b9369b976fc0706c20f790a540d3 https://doi.org/10.9790/1676-09445160 Zobrazit plný text záznamu