Classification of the Human Development Index in Indonesia Using the Bootstrap Aggregating Method

Successful development of the quality of human life in a region is determined by the Human Development Index (HDI). Human development performance based on the HDI can be measured: long and healthy life, knowledge, and a decent standard of living. The HDI is usually grouped into several categories to facilitate the classification of the HDI level of each region. This study aimed to determine the ability of the bootstrap aggregating (bagging) method to classify the HDI by district/city. Bagging is a stochastic machine learning approach that can eliminate the variance of the classifier by producing a bootstrap ensemble to obtain better accuracy results. The dependent variable in this study was the HDI by district/city in 2020. In contrast, life expectancy at birth, expected years of schooling, mean years of schooling, and real expenditure per capita are adjusted as independent variables. Bagging was applied to the high and low categories of HDI data. The bagging method demonstrated good classification performance due to only eight classification errors, namely the HDI data which should be in the high category but classified into the low category by the bagging method. Based on the results of calculations with 25 replications, it can be concluded that the bagging method has a very good performance, with an accuracy value of 92.3%, the sensitivity of 100%, and specificity of 83.33%. The bagging method is considered very good for the classifying the HDI by district/city in Indonesia in 2020 because it has a balanced accuracy of 91.67%.

Prediction of Cardiovascular Disease on Different Parameters Using Machine Learning

The most common serious diseases affecting human health are cardiovascular diseases (CVDs). Early diagnosis can prevent or mitigate CVDs, which can reduce the rate of death. It's a promising approach to identify risk factors using machine learning models. We wish to propose a model with different methods to effectively predict heart disease. We have employed effective data collection, data pre-processing and data transformation methods for the precise information of our training model to make our proposed model a success. A combined dataset has been used (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). The appropriate function is selected using AASSO (Advanced Absolute Shrinkage and Selection Operator techniques) and AASSO techniques. Appropriate features are selected. New hybrids are developed with integration of the traditional bagging and boosting methods, such as Decision Tree Bagger Method (DTBM), the Random Forest Bagging Method (RFBM), the K-Nearest Neighbour Bagging method (KNNBM), the AdaBoost Boosting Method (ABBM), and the GBBM. Our machine learning algorithms, along with Negative Predictive Value (NGR, false positive rates), and false negative flow rates, also were implemented to calculate accuracy of our model, sensitivity (SEN), error rate, accuracy of the model (FRE) and the F1 score (F1) (FNR). The results are shown for comparisons separately. Based on the result analysis, our proposed model produced the highest precision, Accuracy using RFBM and relief selection methods (99.05 percent).

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value

Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.

The effect of hexagonal nano boron nitride on mechanical performances and machinability behaviors of basalt fabric reinforced epoxy composites

The aim of this work is to investigate the effect of the hexagonal nano boron nitride (h-BN) doped epoxy on the mechanical performances and machinability behaviors of basalt fiber (BF) composites. The h-BN has used as a dopant with a weight rate of 1 wt. %. The polymer composites were prepared by the vacuum bagging method and specimens were processed with abrasive water jet machining (AWJM) to analyze the machinability of the material. A circular hole with an 8 mm size was machined by abrasive water jet (AWJ) on the composite plate with a thickness of 2.3 ± 0.1 mm using the garnet size of 80 µm as an abrasive. Taper angle and circularity of machined hole, the entry-exit hole diameter, delamination, splintering, burring, thickness, and hardness were measured. Results indicate that cutting speed has a high influence on the taper angle and circularity respectively.

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.

Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. Conclusion: This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.