Kolmogorov-Smirnov and continuous ranked probability score validation on the Bayesian model averaging for microarray data

Astuti, Ani Budi; Iriawan, Nur; Irhamah,; Kuswanto, Heri

doi:10.12988/ams.2014.49760

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…So that 1 can be written as: , the posterior probability that will be used for decision making is proportional to the product of the likelihood function and the prior probability of the model parameters ( [1], [20], [21], [22], [23], and [24]). …”

Section: Methodsmentioning

confidence: 99%

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Astuti

Iriawan

Irhamah

et al. 2017

J. Phys.: Conf. Ser.

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Abstract. Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept. IntroductionWhen a set of real data observed have a very small amount and a multimodal distribution then it is necessary need specially handling. The Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. These approach is based directly on the posterior distribution of the data. Therefore Bayesian approach can accommodate the real condition of the data based on the data driven concept and [5]). Bayesian Mixture Model (BMM) is one model that uses a Bayesian approach for multimodal models and its parameter models is seen as random variables in the model parameter space [6].Various studies have previously been carried out by BMM modeling. The modeling of BMM for the DM data in Surabaya is the research that has been done by [7] and the research of [8] has been conducted for the DM data in Malang by BMMA modeling. Some of the BMM research in the other field, among others [9] and [10]. Modeling of cases is very important in order to know the exact model and can be used for decision making in accordance with the cases observed [11].

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Section: Methodsmentioning

confidence: 99%

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Astuti

Iriawan

Irhamah

et al. 2017

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the classifier, it can be done by measuring accuracy and specificity (Baratloo et al, 2015;Astuti et al, 2014). The accuracy describes the total classification of the data that are classified correctly by the classifier, where higher accuracy means better classification.…”

Section: Accuracymentioning

confidence: 99%

Comparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector Machine

Kuswanto

Sarumaha

Ohwada³

2017

Journal of Computer Science

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Quality of a product determines the customer loyalty and it can be measured by conducting a survey. Company 'X' that sells three kinds of product (low, medium and high price) collected very large dataset through an online survey and recorded customer defection and their characteristic. The measured variables are Update Accumulation, Product Price, Customer Type, Delivery Status and Customer Defection. The data has an imbalanced response that could mislead the accuracy of classification if it is analyzed using standard approaches. Selective Sampling (SS) and Random Undersampling (RU) have been applied to draw a sample from imbalance response in order to obtain more balance data. Furthermore, Support Vector Machine (SVM) has been applied to classify the sampled data. The performance of the SS-SVM and SS-RU to classify sampled data has been evaluated and compared with the result of classifying the raw dataset. The RU yields on exact balance (50%:50%) response class, while SS reduce the imbalance proportion significantly (around 52%:48%). Nevertheless, the SS-SVM outperforms RU-SVM in the sense that it is capable to run the process effectively, where the SS-SVM reduces the duration of classification process 3 to 20 h shorter than using RU-SVM, with slightly different accuracy rate. Moreover, the SS-SVM maintains the basic characteristics of raw data better than RU-SVM.

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“…BMA is a Bayesian solution for uncertainty model, where the completion of the model is done by averaging the posterior distribution of all best models (Madigan and Raftery, 1994;Montgomery and Nyhan, 2010;Kuswanto and Sari, 2013;Astuti et al, 2014;2015).…”

Section: Full Conditional Distributionsmentioning

confidence: 99%

“…Bayesian method had been proposed as a statistical analysis that did not consider the number of samples and hence this method could be used for analyzing small or large number of samples with any distribution (Congdon, 2006;Ghosh et al, 2006;Ahmed et al, 2010;Anggorowati et al, 2012;Diana et al, 2013;Amran et al, 2013;Astuti et al, 2014;2015;Yuan-Ying et al, 2014). All parameters in the model are treated as random variable in Bayesian method (Gelman et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Determining the Number of Mixture Components on the Bayesian Mixture Model Averaging for Microarray Data

Astuti¹,

Iriawan²,

Irhamah³

et al. 2015

Journal of Mathematics and Statistics

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The major challenges on the statistical analysis of microarray data are the limited availability of samples, large number of measured variables and the complexity of distribution of the data obtained (e.g., multimodal). These phenomena could be considered in Bayesian method, used Bayesian Mixture Model (BMM) methods and Bayesian Model Averaging (BMA) methods. Modeling of Bayesian Mixture Model Averaging (BMMA) for microarray data was developed based on these two studies. One of the most important stages in BMMA is determination of the number of mixture components in the data setting as the most appropriate BMMA models. This paper proposes an algorithm for determining the number of mixture components in BMMA for microarray data. The algorithm is developed based on the simulation data generated from a case study of Indonesian and it has been implemented on the outside Indonesian microarray data. The results have succed to demonstrate two step algorithms, called Preliminary Process and Smoothing Process Algorithms, to the Indonesian case microarray data with the accuracy rate of 99.3690% and 99.9094% for the outside Indonesian microarray data.

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Kolmogorov-Smirnov and continuous ranked probability score validation on the Bayesian model averaging for microarray data

Cited by 7 publications

References 14 publications

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Comparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector Machine

An Algorithm for Determining the Number of Mixture Components on the Bayesian Mixture Model Averaging for Microarray Data

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