Adaptive Multi codebook Fuzzy Neuro Generalized Learning Vector Quantization for sleep stages classification

Indra Hermawan, M. Iqbal Tawakal, I. Made Agus Setiawan, Ikhsanul Habibie, Wisnu Jatmiko

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

In this paper, a new codebook based learning method, Adaptive Multicodebook Fuzzy Neuro Generalized Learning Vector Quantization (FNGLVQ), is proposed. The main contribution of this paper is the use of multi codebook which is adaptive in nature to the distribution of the data. The number and position of the codebook is determined through clustering approach. In this research, a decision tree based clustering, CLTree, is used to cluster the data to get the initial placement of the codebook. The advantage of using CLTree against other clustering method is CLTree do not need the number of cluster as initial input. In average, this method improves the accuracy rate of Mitra data 3 and 4 class 2% and 2.12%, respectively compared to the single codebook approach.

Original languageEnglish
Pages431-436
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013 - Bali, Indonesia
Duration: 28 Sep 201329 Sep 2013

Conference

Conference2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013
Country/TerritoryIndonesia
CityBali
Period28/09/1329/09/13

Fingerprint

Dive into the research topics of 'Adaptive Multi codebook Fuzzy Neuro Generalized Learning Vector Quantization for sleep stages classification'. Together they form a unique fingerprint.

Cite this