An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis

Yilmaz, Ersen

doi:10.1155/2013/849674

Cited by 30 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The filter-based selection method includes the t-test, factor analysis, and stepwise regression, which assess the relevance of the variables according to pre-determined indices. The proposed criteria for this method can be Fisher score (Yjlmaz, 2013), Laplacian score (Wan et al, 2015) and F-score (Chen & Lin, 2003). Among those criteria, F-score is considered to be the simplest (Song et al, 2017).…”

Section: Review Of Predictors and Predictor Selectionmentioning

confidence: 99%

Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

Vu¹,

Vu²,

Nguyen

et al. 2019

Investment Management and Financial Innovations

View full text Add to dashboard Cite

The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms.

show abstract

Section: Review Of Predictors and Predictor Selectionmentioning

confidence: 99%

Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

Vu¹,

Vu²,

Nguyen

et al. 2019

Investment Management and Financial Innovations

View full text Add to dashboard Cite

show abstract

“…There is a growing tendency to use clinical decision support systems in medical diagnosis. These systems help to optimize medical decisions, improve medical treatments, and reduce financial costs [ 1 , 2 ]. A large number of the medical diagnosis procedures can be converted into intelligent data classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree

Yilmaz

Kılıkçıer

2013

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.

show abstract

“…r (Pearson) benzerlik ölçütü eşleştirilmiş veriler arasındaki doğrusal bir ilişki gücünün istatistiksel ölçüsüdür [1]. Fisher Score grupların ortalamaları arasında uzaklık ile doğru varyanslarının toplamı ile ters orantılı mesafe ölçüm yöntemidir [2]. Mutual Information (ortak bilgi) bir rastgele değişkenin diğer bir rastgele değişken hakkında ne kadarlık nicel bilgi sakladığını ölçer [3].…”

Section: Abstract-eeg Visual Stimuli Feature Extraction Channel Seunclassified

Comparison of channel selection methods on the classification of EEG data obtained from the animal non-animal categorization experiment

Özbeyaz

Arıca

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

View full text Add to dashboard Cite

Özetçe-Bu çalışmada görsel uyaran olarak canlı ve cansız nesne resimlerinin kullanıldığı deneylerden elde edilen Elektroansefalogram (EEG) işaretlerinin ikili sınıflandırma sürecinde kanal seçim modellerinin sınıflandırma performansını nasıl etkilediği araştırılmıştır. Deneyde kullanılan farklı elektrotlardan gelen işaretler analiz edilmiş ve görsel uyaranlar ile ilgili olan kanallar tespit edilmeye çalışılmıştır. Yapılan analiz çalışmalarında özellik çıkarım yöntemi olarak Parçalı Sabit Modelleme (PCM) ve Parçalı Lineer Modelleme (PLM) teknikleri, kanal seçim yöntemleri olarak r (Pearson) benzerlik ölçütü, Fisher Score (FS), Mutual Information (MI), Kullback Leibler Distance (KLD), Varyans Oran (VR) ve Ortak Uzamsal Model (CSP) yöntemleri kullanılmıştır. Yapılan sınıflandırma sonucunda özellik çıkarım yöntemi olarak PCM ve kanal seçim yöntemi olarak VR kullanıldığında iyi bir sınıflandırma performansının oluştuğu gözlemlenmiştir.

show abstract

An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis

Cited by 30 publications

References 13 publications

Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree

Comparison of channel selection methods on the classification of EEG data obtained from the animal non-animal categorization experiment

Contact Info

Product

Resources

About