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

Hermawan, Indra; Tawakal, Muhammad Iqbal; Setiawan, I Made Agus; Habibie, Ikhsanul; Jatmiko, Wisnu

doi:10.1109/icacsis.2013.6761614

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The accuracy of these classifiers are 49.96%, 60.82%, 61.82%, 61.82%, and 59.20% for SVM, Naive Bayes, MLP, kNN, and J48. From these results, it can be concluded that This experiment using the sleep dataset was conducted to show the improvement of the proposed algorithm compared to the AFNGLVQ and FNGLVQ previously proposed on [41]. Besides, the rest of the commonly used algorithm was also tested in the sleep dataset.…”

Section: Results From Arrhythmia Data Setmentioning

confidence: 97%

“…The PI membership function itself is useful to accurately solve a difficult case where the feature value has a degree of skewness. Note that we have previously proposed and conducted an early experiment on multi-codebook AFNGLVQ [41]. However, its performance gain was not substantial, and therefore, we chose the original version of AFNGLVQ (i.e., the non-multi-codebook) to be further developed in this study.…”

Section: Introductionmentioning

confidence: 99%

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Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)

et al. 2021

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In a real-world environment, there are several difficult obstacles to overcome in classification. Those obstacles are data overlapping and skewness of data distribution. Overlapping data occur when many data from different classes overlap with each other; this condition often occurs when there are many classes in a data set. On other hand, skewness of data distribution occurs when the data distribution is not a Gaussian (normal) distribution. To overcome these two problems, a new method called Adaptive Fuzzy-Neuro Generalized Learning Vector Quantization using PI membership function (AFNGLVQ-PI) is proposed in this study. AFNGLVQ-PI is derived from Fuzzy-Neuro Generalized Learning Vector Quantization using the PI membership function (FNGLVQ-PI). In FNGLVQ-PI, the updated values for minimum and maximum variables in the fuzzy membership function are set based on the mean of the updated values. Whereas, in the newly proposed AFNGLVQ-PI, updated values for minimum, maximum, and mean variables are derived based on the differential equations to approximate the data distribution better. In this study, the newly proposed AFNGLVQ-PI algorithm was tested and verified on twelve different data sets. Two of the data sets are synthetic data sets where we could compare the performance of the data sets in different overlapping conditions and levels of skewness. The rest of the data sets were chosen and used as a benchmark to compare the performance of the proposed algorithm. In the experiment, AFNGLVQ-PI took first place in 18 out of 29 experiments. Furthermore, AFNGLVQ-PI also achieved positive improvements for all data sets used in the experiments, which could not be achieved by the Learning Vector Quantization (LVQ), Generalized Learning Vector Quantization (GLVQ), and other commonly used algorithms, such as SVM, kNN, and MLP.

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Section: Results From Arrhythmia Data Setmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)

et al. 2021

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“…Eka [17]. Cheng Sun developed portable 12-lead ECG acquisition system [18]. ECG signal, is not only used for analyzing heart disease, but also other purpose i.e.…”

Section: Introductionmentioning

confidence: 99%

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Ma’sum¹,

Imah²,

Gunawan³

2014

Jurnal Ilmu Komputer dan Informasi

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Abstract Heart disease is the number one deadly disease in Indonesia. One of the main causes of fatality is the late detection of the disease. To avoid escalation of mortality caused by heart disease, we need early detection and monitoring system of heart disease. Therefore, in this research we propose an early detection and monitoring system of heart disease based on ECG signal. The proposed system has three main components: ECG hardware, smartphone, and server. Since the proposed system is designed to classify heartbeat signal, heart disease symptom can be detected as early as possible. We use FLVQ-PSO algorithm to classify heartbeat signal. Experiment result shows that classification accuracy of the system can reach 91.63%. Moreover, the proposed system can be used to verify patients heartbeat by cardiologists from distant area (telehealth). Experiment result shows that responsiveness of the system for the telehealth system is less than 0.6 seconds.

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

Cited by 2 publications

References 14 publications

Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)

Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)

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