A supervised machine learning algorithm for arrhythmia analysis

Güvenir, H. Altay; Açar, Burak; Demiroz, Gulsen; Cekin, A.

doi:10.1109/cic.1997.647926

Cited by 130 publications

(99 citation statements)

References 4 publications

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“…The original dataset has 16 class values: one for healthy items, 14 types of cardiac arrhythmias and one class value for unclassified items [33]. We erased the unclassified items and built a binary class (normal vs. arrhythmia).…”

Section: Mi(x Y) = P(x V) Log-^--dxdy X Y Pv^jpyy)mentioning

confidence: 99%

Directional naive Bayes classifiers

López-Cruz

Bielza

Larrañaga

2013

Pattern Anal Applic

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Directional data are ubiquitous in science. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises-Fisher distributions, should be used to deal with this kind of information. We extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables follow either of these distributions. We consider the simple sce-nario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and direc-tional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are then evaluated over eight datasets, showing competitive performances against other naive Bayes classifiers that use Gaussian distributions or discretization to manage directional data.

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Section: Mi(x Y) = P(x V) Log-^--dxdy X Y Pv^jpyy)mentioning

confidence: 99%

Directional naive Bayes classifiers

López-Cruz

Bielza

Larrañaga

2013

Pattern Anal Applic

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“…Moreover, the rich information provided by the latter can be used to predict and detect the onset of arrhythmias by analysing electric impulse patterns [22]. In the present study I evaluate the proposed fusion methodology in the context of this prediction.…”

Section: Arrhythmia Predictionmentioning

confidence: 99%

“…Table 1. An illustration of the adopted arrhythmia dataset, originally collected and described in detail by Guvenir et al [22]. It comprises 279 input variables, of which only a small selection is shown here, and the corresponding target variable which for the purpose of the present experiment can be considered to be binary valued, taking on the value 0 when arrhythmia is not diagnosed and 1 when it is.…”

Section: Arrhythmia Predictionmentioning

confidence: 99%

“…The dataset comprises a rich set of demographic and antropometric variables, such as each person's age, sex, height, and bodyweight, as well as a variety of features extracted from the person's ECG, including the heart rate and a series of characteristics of the corresponding signal deflections (the duration of the QRS interval, amplitudes of Q, R, and S waves etc. ); please refer to the original publication for full detail [22]. The target variable for the purpose of the present experiment can be considered to be binary valued, taking on the value 0 when arrhythmia is not present and 1 when it is; please see Table 1.…”

Section: Arrhythmia Predictionmentioning

confidence: 99%

“…I used the dataset collected by Guvenir et al [22] which is freely available online and can be downloaded [23]. The dataset comprises a rich set of demographic and antropometric variables, such as each person's age, sex, height, and bodyweight, as well as a variety of features extracted from the person's ECG, including the heart rate and a series of characteristics of the corresponding signal deflections (the duration of the QRS interval, amplitudes of Q, R, and S waves etc.…”

Section: Arrhythmia Predictionmentioning

confidence: 99%

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Abstract:The ever-increasing demand for a reliable inference capable of handling unpredictable challenges of practical application in the real world has made research on information fusion of major importance; indeed, this challenge is pervasive in a whole range of image understanding tasks. In the development of the most common type-score-level fusion algorithms-it is virtually universally desirable to have as a reference starting point a simple and universally sound baseline benchmark which newly developed approaches can be compared to. One of the most pervasively used methods is that of weighted linear fusion. It has cemented itself as the default off-the-shelf baseline owing to its simplicity of implementation, interpretability, and surprisingly competitive performance across a widest range of application domains and information source types. In this paper I argue that despite this track record, weighted linear fusion is not a good baseline on the grounds that there is an equally simple and interpretable alternative-namely quadratic mean-based fusion-which is theoretically more principled and which is more successful in practice. I argue the former from first principles and demonstrate the latter using a series of experiments on a diverse set of fusion problems: classification using synthetically generated data, computer vision-based object recognition, arrhythmia detection, and fatality prediction in motor vehicle accidents. On all of the aforementioned problems and in all instances, the proposed fusion approach exhibits superior performance over linear fusion, often increasing class separation by several orders of magnitude.

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A new modification and application of item response theory‐based feature selection for different machine learning tasks

Çoban

2022

Concurrency and Computation

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Feature selection (FS) is an important step of the existing machine learning (ML) methodology since it often makes it possible to obtain better results using a lower number of features. Hence, in the literature, there exist many studies aiming at proposing a new FS method or improving an existing one in the context of ML. Accordingly, this study presents a new lossy modification of a feature selector which is a specific type of filter‐based FS and depends on item response theory. This method computes feature importance in a supervised manner and is previously employed for classical text categorization (TC) task, where it was shown that the selector provided satisfying results on high dimensional and benchmark text datasets. As such, this paper introduces a new modification of this selector along with its new variants and investigates its applicability for different ML tasks other than TC. Experimental results are obtained on 35 different datasets, of which nine are well‐known and real‐world datasets from the UCI ML repository. Our comparative results with the most popular filter‐based FS methods show that it is possible to obtain better results with this new modified selector or one of its variants on the majority of both binary and real‐world datasets compared to its well‐known peers.

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A supervised machine learning algorithm for arrhythmia analysis

Cited by 130 publications

References 4 publications

Directional naive Bayes classifiers

Directional naive Bayes classifiers

Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better)

A new modification and application of item response theory‐based feature selection for different machine learning tasks

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