Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search

This paper investigates the Nested Monte Carlo Tree Search (NMCTS) for feature selection on regression tasks. NMCTS starts out with an empty subset and uses search results of lower nesting level simulation. Level 0 is based on random moves until the path reaches the leaf node. In order to accomplish feature selection on the regression task, the Gamma test is introduced to play the role of the reward function at the end of the simulation. The concept Vratio of the Gamma test is also combined with the original UCT-tuned1 and the design of stopping conditions in the selection and simulation phases. The proposed GNMCTS method was tested on seven numeric datasets and compared with six other feature selection methods. It shows better performance than the vanilla MCTS framework and maintains the relevant information in the original feature space. The experimental results demonstrate that GNMCTS is a robust and effective tool for feature selection. It can accomplish the task well in a reasonable computation budget.

An Extension of the Gamma Test Statistics to Binary Variables and Some Applications

In this paper, we discuss the problem of estimating the minimum error reachable by a regression model given a dataset, prior to learning. More specifically, we extend the Gamma Test estimates of the variance of the noise from the continuous case to the binary case. We give some heuristics for further possible extensions of the theory in the continuous case with the [Formula: see text]-norm and conclude with some applications and simulations. From the point of view of machine learning, the result is relevant because it gives conditions under which there is no need to learn the model in order to predict the best possible performance.

Performance of a lymphocyte t interferon gamma test (Quantiferon-TB gold in tube) in the diagnosis of active tuberculosis in HIV-infected children

Objective Evaluate the performance of QuantiFERON ® -TB Gold In-Tube test (QFT-GIT), to improve the diagnosis of active tuberculosis (TB) in Human Immuno-Deficiency Virus (HIV)-infected children. Method Sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) of QFT-GIT were assessed in 58/63 HIV-infected children who were suspected of having TB. Results Sensitivity of QFT-GIT was 20.69%, specificity 96.55%, PPV/NPV respectively 85.71% and 54.90%. Conclusion QFT-GIT appears to be of little contribution to the diagnosis of active TB in children living with HIV in a TB-endemic country.

PENGETAHUAN DAN MOTIVASI ORANGTUA DALAM MEMBERIKAN VAKSIN PENCEGAH PNEUMONIA (HIB, PCV DAN CAMPAK) PADA BALITA DI YOGYAKARTA

Background: Pneumonia is a disease that is a problem of high morbidity and mortality in children under 5 years of age. The incidence of pneumonia in Indonesia increased by 2.0% in 2018, Yogyakarta area the incidence of pneumonia by 26.61% in 2017. Parents are an important target for their children's health. The importance of knowledge and motivation to reduce the incidence of pneumonia in children. Objective: Knowing the relationship of knowledge with parents' motivation in providing Hib, PCV and Measles vaccines to prevent under five pneumonia in PKU Muhammadiyah Hospital, Yogyakarta City. Method: This research uses descriptive quantitative design with cross sectional approach. Sampling was carried out with a purposive sampling technique of 60 respondents. Retrieval of data using knowledge and motivation questionnaires and analyzed using the Gamma test. Result: Knowledge with parental motivation in preventing toddler pneumonia in PKU Muhammadiyah Hospital Yogyakarta obtained a level of parental knowledge of 66.7% in the good category and parental motivation of 76.7% in the high category. Gamma test results obtained p-value of 0.010 with a correlation coefficient of 0.587. Conclusions: There is a significant correlation between knowledge and parents' motivation in giving Hib, PCV and Measles vaccines to prevent under five pneumonia in PKU Muhammadiyah Hospital in Yogyakarta City with moderate relationship closeness.

Prediction of soil macronutrients using fractal parameters and artificial intelligence methods

Aim of study: To evaluate artificial neural networks (ANN), and k-Nearest Neighbor (k-NN) to support vector regression (SVR) models for estimation of available soil nitrogen (N), phosphorous (P) and available potassium (K).Area of study: Two separate agricultural sites in Semnan and Gorgan, in Semnan and Golestan provinces of Iran, respectively.Material and methods: Complete data set of soil properties was used to evaluate the models’ performance using a k-fold test data set scanning procedures. Soil property measures including clay, sand and silt content, soil organic carbon (SOC), electrical conductivity (EC), lime content as well as fractal dimension (D) were used for the prediction of soil macronutrients. A Gamma test was utilized for defining the optimum combination of the input variables.Main results: The sensitivity analysis showed that OC, EC, and clay were the most significant variables in the prediction of soil macronutrients. The SVR model was more accurate compared to the ANN and k-NN models. N values were estimated more accurately than K and P nutrients, in all the applied models.Research highlights: The accuracy of models among the test stages illustrated that using a single data set for investigation of model performance could be misleading. Therefore, the complete data set would be necessary for suitable evaluation of the model.