Exploring Factors Associated with Voucher Program for Speech Language Therapy for the Preschoolers of Parents with Communication Disorder using Weighted Random Forests

Byeon, Haewon; Cha, Sulki; Lim, KyoungYuel

doi:10.14569/ijacsa.2019.0100503

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…This method is motivated by the potential instability of averaging predictions of trees. Byeon et al [15] used Out-of-Bag (OOB) samples for deriving Akaike weights while averaging the tree results. Based on the proposed algorithm they analyzed in-depth the consumers' demand level in order to operate "the Voucher Program for Speech Language Therapy" efficiently and for suggesting different ways to improve the special education service support.…”

Section: Literature Reviewmentioning

confidence: 99%

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Weighted Random Forests to Improve Arrhythmia Classification

et al. 2020

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Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

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Section: Literature Reviewmentioning

confidence: 99%

“…Pruning of individual trees in the forest [12,13]; • Weighing individual trees [11,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Weighted Random Forests to Improve Arrhythmia Classification

et al. 2020

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“…Pham and Olafsson [ 44 ] weighted each tree by replacing the regular average with a Cesaro average. Weighting based on the variable performance and importance was developed by Byeon et al [ 45 ] and Booth et al [ 46 ]. Finally, Utki et al [ 47 ] created a weighted random survival forest by assigning weights to survival decision trees or to their subsets.…”

Section: Preliminariesmentioning

confidence: 99%

Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case

Gostkowski

Gajowniczek

2020

Entropy

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Due to various regulations (e.g., the Basel III Accord), banks need to keep a specified amount of capital to reduce the impact of their insolvency. This equity can be calculated using, e.g., the Internal Rating Approach, enabling institutions to develop their own statistical models. In this regard, one of the most important parameters is the loss given default, whose correct estimation may lead to a healthier and riskless allocation of the capital. Unfortunately, since the loss given default distribution is a bimodal application of the modeling methods (e.g., ordinary least squares or regression trees), aiming at predicting the mean value is not enough. Bimodality means that a distribution has two modes and has a large proportion of observations with large distances from the middle of the distribution; therefore, to overcome this fact, more advanced methods are required. To this end, to model the entire loss given default distribution, in this article we present the weighted quantile Regression Forest algorithm, which is an ensemble technique. We evaluate our methodology over a dataset collected by one of the biggest Polish banks. Through our research, we show that weighted quantile Regression Forests outperform “single” state-of-the-art models in terms of their accuracy and the stability.

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“…Previous studies [1,10,11,12,13] that evaluated the factors related to geriatric depression in South Korea local communities reported that health, socioeconomic status, education level, age, spouse, and social activities affected geriatric depression. Since regression analysis was mainly used as a modeling method to predict depression, they were efficient in identifying individual risk factors [14,15]. However, they were limited in identifying compound-risk factors (multivariate) such as sociodemographic variables and living habits [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Since regression analysis was mainly used as a modeling method to predict depression, they were efficient in identifying individual risk factors [14,15]. However, they were limited in identifying compound-risk factors (multivariate) such as sociodemographic variables and living habits [14,15]. Moreover, since regression analysis assumes independence, normality, and homoscedasticity, there is a possibility of producing biased results when the model is developed using data in violation of normality [16].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Byeon¹

2021

IJACSA

Self Cite

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Since the number of healthy people is much more than that of ill people, it is highly likely that the problem of imbalanced data will occur when predicting the depression of the elderly living in the community using big data. When raw data are directly analyzed without using supplementary techniques such as a sample algorithm for datasets, which have imbalanced class ratios, it can decrease the performance of machine learning by causing prediction errors in the analysis process. Therefore, it is necessary to use a data sampling technique for overcoming this imbalanced data issue. As a result, this study tried to identify an effective way for processing imbalanced data to develop ensemble-based machine learning by comparing the performance of sampling methods using the depression data of the elderly living in South Korean communities, which had quite imbalanced class ratios. This study developed a model for predicting the depression of the elderly living in the community using a logistic regression model, gradient boosting machine (GBM), and random forest, and compared the accuracy, sensitivity, and specificity of them to evaluate the prediction performance of them. This study analyzed 4,085 elderly people (≥60 years old) living in the community. The depression data of the elderly in the community used in this study had an unbalance issue: the result of the depression screening test showed that 87.5% of subjects did not have depression, while 12.5% of them had depression. This study used oversampling, undersampling, and SMOTE methods to overcome the unbalance problem of the binary dataset, and the prediction performance (accuracy, sensitivity, and specificity) of each sampling method was compared. The results of this study confirmed that the SMOTE-based random forest algorithm showing the highest accuracy (a sensitivity ≥ 0.6 and a specificity ≥ 0.6) was best prediction performance among random forest, GBM, and logistic regression analysis. Further studies are needed to compare the accuracy of SMOTE, undersampling, and oversampling for imbalanced data with high dimensional y-variables.

show abstract

Exploring Factors Associated with Voucher Program for Speech Language Therapy for the Preschoolers of Parents with Communication Disorder using Weighted Random Forests

Cited by 8 publications

References 23 publications

Weighted Random Forests to Improve Arrhythmia Classification

Weighted Random Forests to Improve Arrhythmia Classification

Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

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