Time series classification using k-Nearest Neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F12%3A00183197" target="_blank" >RIV/62156489:43110/12:00183197 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Time series classification using k-Nearest Neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

Popis výsledku v původním jazyce

We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification -- statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL), an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ) algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM). After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to), we used, with a good experience in former studies, musical excerpts as a source of real-wo

Název v anglickém jazyce

Time series classification using k-Nearest Neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

Popis výsledku anglicky

We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification -- statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL), an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ) algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM). After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to), we used, with a good experience in former studies, musical excerpts as a source of real-wo

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Svazek periodika

60

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

4

Strana od-do

69-72

Kód UT WoS článku

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EID výsledku v databázi Scopus

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