Application Of Bagging Research Articles

Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies.In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

IDENTIFICATION OF ROOTKITS IN NETWORK TRAFFIC WITH USING THE BAGGING OF CLASSIFIERS

The paper proposes an approach to identify anomalies in network traffic based on the use of machine learning classifiers. The solution allows you to determine the resulting state class by averaging the votes of individual classifiers. The approach was evaluated on the NSL-KDD public dataset. A compar- ison of the performance of classifiers and their averaged evaluation using the Weka tool was performed. The NSL-KDD set has been optimized, with an emphasis on "rootkit" type attacks, as one of the most diffi- cult types of attacks to detect. Using the bagging-based approach implemented in the Weka application, it was possible to obtain accuracy results – 99.94%. During the experiment, a tendency of increasing accu- racy in the application of bagging on open data was revealed as the volume of training data increases. The proposed approach can be applied in the design of systems for detecting attacks and other abnormal states of information systems. The results of the accuracy of the average assessment require further re- search in order to improve the indicators. It is possible to modernize the approach of averaging the votes of classifiers by excluding/adding other classifiers, qualitative selection of attributes and their features, in- creasing the number of training samples for classification.

Effect of Pre-harvest Application on Physical Attributes of Guava (Psidium guajava L.) in cv. L–49: Plant Growth Regulators and Bagging

The present investigation was conducted to study the Effect of pre-harvest application of plant growth regulators (PGR) and bagging on Physical attributes of Guava (Psidium guajava L.) in cv. L - 49. The research was carried out at the main experiment station Horticulture, Department of Fruit Sciences, Acharya Narendra Deva University of Agriculture and Technology, Narendra Nagar, Kumarganj, Ayodhya 224229 (U.P.) India during the year 2022-23. The experiment was conducted in a Randomized Block Design with three replications and 8 treatments GA3 (50 ppm) and NAA (20 ppm) and GA3 (50 ppm) +NAA (20 ppm), GA3 (50 ppm) with bagging, and NAA (20 ppm) with bagging. GA3 (50 ppm) + NAA (20 ppm) with bagging, bagging (white polythene), and control applied on spray 45 days before harvesting. It is concluded that among the different treatments. T6 GA3 (50 ppm) + NAA (20 ppm) with bagging, was recorded maximum Fruit length (4.90 cm), Fruit width (6.62 cm), Fruit weight (158.20 gram), Fruit volume (161.60 cm3), Specific gravity (0.98 g/cm3) therefore, recommended for application to the Guava tree in the sub-tropical region of Uttar Pradesh in order to obtain better quality fruit.

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

The increase in positive cases of COVID-19 not only affects the health and lifestyle, but also the economy and the stock market. Tech and digital sector stocks can be predicted to be one of the most profitable. Therefore, the prediction of the stock price is required to be able to forecast the prospects of investment in the future. In this study, the prediction of the stock prices of Multipolar Technologies Ltd. (MLPT) was carried out using the support vector regression (SVR) method with bootstrap aggregation (bagging) technique and particle swarm optimisation (PSO) as SVR optimisation. From the results of the prediction process, it is shown that the application of bagging and PSO techniques in predicting stock prices in the technological sector can reduce the root mean squared error (RMSE) value on the SVR, the RMSE value from 22.142 to 21.833. Although it does not have a big impact, it is better to apply a combination of bagging and PSO techniques to SVR than one of them (SVR/SVR - PSO/SVR-Bagging).

Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

Application of bagging in day-ahead electricity price forecasting and factor augmentation

The electricity price forecasting (EPF) is a challenging task not only because of the uncommon characteristics of electricity but also because of the existence of many potential predictors with changing predictive abilities over time. In such an environment, how to account for all available factors and extract as much information as possible is the key to the production of accurate forecasts. To address this long-standing issue in a way that balances complexity and forecasting accuracy while facilitating the traceability of the predictor selection procedure, we propose the method of Bootstrap Aggregation (bagging). To forecast day-ahead electricity prices in a multivariate context for six major power markets, we construct a large-scale pure price model and apply the bagging approach in comparison with the popular Least Absolute Shrinkage and Selection Operator (LASSO) estimation method. Our forecasting study reveals that bagging provides substantial forecast improvements on daily and hourly scales in almost all markets over the popular LASSO estimation method. The differentiation in the forecast performances of the two approaches appears to arise, inter alia, from their structural differences in the explanatory variables selection process. Moreover, to account for the intraday hourly dependencies of day-ahead electricity prices, all our models are augmented with latent factors, and a substantial improvement is observed only in the forecasts from models covering a relatively limited number of predictors.

Preharvest Bagging as an Alternative Technique for Enhancing Fruit Quality: A Review

Preharvest factors such as poor orchard management and field sanitation can lead to pathological infection of the tree fruit being grown as well as insect pest infestation, resulting in poor postharvest fruit quality. Wind and hail damage may cause significant tree fruit abrasions and blemishes. Consequently, these preharvest factors may reduce yield and cause market and economic losses. One of the most successful methods used to manage tree fruit pathogens and insect infestation is the application of agrochemicals, predominantly fungicides and insecticides. However, this method has recently been criticized due to the adverse effects on field workers’ safety, consumers’ health, and the environment. The development and use of preharvest bagging are among the most environmentally friendly technologies intended for safe enhancement of tree fruit quality. The technique protects tree fruit against pathogens, insect pests, physiological disorders, agrochemical residues, fruit abrasions, sunburn, and bird damage, and it further modifies the microenvironment for fruit development with its various beneficial effects on its external and internal quality. Furthermore, because of the global restrictions of agrochemicals and social awareness, this technique provides extensive relief to growers and consumers. However, bagging is labor-intensive and expensive; therefore, its benefits or advantages and disadvantages must be thoroughly investigated if it is to be promoted commercially. This review examines the improvement of tree fruit quality by the application of preharvest bagging during early stages of fruit growth and development. The latest advances in the development and use of tree fruit bagging and its economic impact and cost–benefit ratio are discussed, as are recommendations for the formulation of bagging materials that could be valuable in the future.

Optimization of C4.5 algorithm using meta learning in diagnosing of chronic kidney diseases

The accuracy in diagnosing a disease is very important and crucial for the hospital institution. The big patient data can be processed into information to diagnose a disease. One of diseases can be diagnosed is chronic kidney disease. The existing data of chronic kidney disease patients can be used for data processing. Data processing is included in the process of data mining. One method that can be implemented to disease diagnosis, namely classification. The algorithm used in this research is decision tree namely C4.5 algorithm. It can be applied in classifying patient data. This study used Chronic Kidney Disease (CKD) dataset. The purpose of this study is optimizing the accuracy of C4.5 algorithm by applying meta learning in diagnosing CKD by comparing the results before and after MultiboostAB and Bagging applied. The validation used 10 fold cross validation. The accuracy is measured by confusion matrix. The combination of C4.5 and MulitboostAB obtained 99.5% accuracy which increased 0.5% from accuracy of C4.5 standalone. Then, C4.5 and Bagging obtained 100% accuracy. The result of this research is the application of bagging on C4.5 algorithm is good in optimizing accuracy.

Genera Acremonium and Sarocladium Cause Brown Spot on Bagged Apple Fruit in China.

Apple fruit spot disease has caused serious economic losses for years in China since the widespread application of fruit bagging in production. Although the three genera Trichothecium, Alternaria, and Acremonium have been reported to be the causal agents, studies on the disease etiology and pathogen biology are still sparse. Here, we report characterization of eight fungal isolates from lesions on 126 symptomatic fruit samples collected in Shaanxi Province, China. Pathogenicity of the isolates was assessed. DNA sequences were obtained at four loci, including D1/D2 domains of the large-subunit nrRNA gene, internal transcribed spacer regions 1 and 2, 5.8S nrDAN gene, a fragment of the actin gene, and a fragment of the β-tubulin. Based on phylogenetic analysis and morphological features, three new species were found: Acremonium mali, Sarocladium liquanensis, and Sarocladium mali. In addition, we made the first report of Sarocladium terricola as a plant pathogen. Temperature and moisture significantly affected in vitro conidial germination of five Acremonium-like species, and their impact on infection of apple fruit was tested using Acremonium sclerotigenum. Conidia of five species germinated from 15 to 35°C in free water; four of the species had optimum temperature around 25°C, whereas conidia of S. terricola had an optimum temperature of 30°C. Conidial germination rate increased as relative humidity (RH) increased. The five isolates had relatively high conidial germination rates at RH > 97%, with a significant decline at 95% RH. Incidence of infection also increased in proportion to RH. In free water, conidial germination was relatively unaffected by temperature.

APPLICATION OF FRUIT BAGGING, SANITATION, AND YELLOW STICKY TRAP TO CONTROL THRIPS ON MANGOSTEEN

Scars on mangosteen fruits caused by thrips are the most prominent constraint in Indonesian export of mangosteen. Most of the exported mangosteen are rejected due to scar appearance. This research aimed to study the effects of fruit bagging, sanitation, and sticky trap application on the scar intensity on mangosteen. The research was conducted at a farmer's polycultured mangosteen orchard in Lima Puluh Kota, West Sumatra, Indonesia from September 2006 to February 2007. The mangosteen trees were cultured with cacao and coconut. To study the effects of bagging on the number of scars, the fruits were individually bagged at different time points starting from the time of calyx falling down (0) followed at 2, 4, 6, 8, 10, 12, and 14 weeks later. Each treatment was replicated six times. The experiment of sanitation (SNT) and yellow fluorescent sticky trap (YST) application was designed in a completely randomized design with six replications, except for the control which was replicated only three times. Four treatments studied were SNT, YST, combination of SNT+YST, and control. The sanitation was applied by removing all weeds under the canopy of mangosteen trees followed with soil tillage and fogging. The sticky trap was nailed on woody sticks and placed on 3 m above the ground at four different positions around the tree canopy. Parameters observed were the percentage and intensity<br />of scars and number of thrips caught on the sticky traps. The results showed that application of early bagging, SNT, YST, and SNT+YST were effective to reduce intensity and percentage of scars. However, combination of SNT+YST demonstrated the best results in reducing the intensity and percentage of scars by 32.14% and 42.82%, respectively. Combination of SNT+YST also drastically decreased thrips population as indicated by the<br />low number of thrips catched after five biweekly observations. Since the fifth observation, the thrips population was low (less than 5 thrips catched per sticky trap) and far below the economic threshold. The present study implies the importance of early fruit bagging, sanitation, and sticky trap application in protecting mangosteen fruits from scars.

APPLICATION OF FRUIT BAGGING, SANITATION, AND YELLOW STICKY TRAP TO CONTROL THRIPS ON MANGOSTEEN

Scars on mangosteen fruits caused by thrips are the most prominent constraint in Indonesian export of mangosteen. Most of the exported mangosteen are rejected due to scar appearance. This research aimed to study the effects of fruit bagging, sanitation, and sticky trap application on the scar intensity on mangosteen. The research was conducted at a farmer's polycultured mangosteen orchard in Lima Puluh Kota, West Sumatra, Indonesia from September 2006 to February 2007. The mangosteen trees were cultured with cacao and coconut. To study the effects of bagging on the number of scars, the fruits were individually bagged at different time points starting from the time of calyx falling down (0) followed at 2, 4, 6, 8, 10, 12, and 14 weeks later. Each treatment was replicated six times. The experiment of sanitation (SNT) and yellow fluorescent sticky trap (YST) application was designed in a completely randomized design with six replications, except for the control which was replicated only three times. Four treatments studied were SNT, YST, combination of SNT+YST, and control. The sanitation was applied by removing all weeds under the canopy of mangosteen trees followed with soil tillage and fogging. The sticky trap was nailed on woody sticks and placed on 3 m above the ground at four different positions around the tree canopy. Parameters observed were the percentage and intensity<br />of scars and number of thrips caught on the sticky traps. The results showed that application of early bagging, SNT, YST, and SNT+YST were effective to reduce intensity and percentage of scars. However, combination of SNT+YST demonstrated the best results in reducing the intensity and percentage of scars by 32.14% and 42.82%, respectively. Combination of SNT+YST also drastically decreased thrips population as indicated by the<br />low number of thrips catched after five biweekly observations. Since the fifth observation, the thrips population was low (less than 5 thrips catched per sticky trap) and far below the economic threshold. The present study implies the importance of early fruit bagging, sanitation, and sticky trap application in protecting mangosteen fruits from scars.

Study of the pesticides distribution in peel, pulp and paper bag and the safety of pear bagging

How to decrease the pesticides residue is a new challenge during growing, storage and processing of agricultural products. Pesticides distribution or migration study would be the key point to find a pesticide removal method. 173 pesticide components in peel, pulp or paper bag were extracted by acetonitrile after fortified with chlorpyrifos-d10 as isotope internal standard. The extract was simply purified by dispersive sorbent of primary secondary amine (PSA) and then measured by online gel permeation chromatography-gas chromatography/mass spectrometry (GPC-GC/MS). The positive results were used to study the pesticides distribution properties in peel, pulp and paper bag. The pesticides distribution properties between peel and pulp were well applied for pesticides removal, risk assessment and suitable eating way for pear. Except of pesticides with strong polarity and high water solubility, most of the pesticides stayed in the skin of a pear and can be eliminated by peeling. A pear is safe for pesticides residue if eaten with peeling and might exist risk to some degree if eaten without peeling. The practical application of pear bagging is safe as far as pesticides residue is concerned.

A Classification Framework Applied to Cancer Gene Expression Profiles

Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF) and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression) increase the prediction accuracy as compared to using gene expression alone.

Is bagging effective in the classification of small-sample genomic and proteomic data?

There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.

Bagging for Gaussian process regression

This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data are re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions. A number of weighting methods for model combination are discussed, including the simple averaging and the weighted averaging rules. We propose to weight the models by the inverse of their predictive variance, and thus the prediction uncertainty of the models is automatically accounted for. The bagging method for Gaussian process regression is successfully applied to the inferential estimation of quality variables in an industrial chemical plant.

Void reduction in autoclave processing of thermoset composites: Part 1: High pressure effects on void reduction

Industrial production of thermoset composite components usually involves the application of a vacuum bagging and autoclave pressure to minimize void percentage, usually to less than 5%. The application of vacuum bagging can be time consuming, with the actual vacuum application limited to a short interval during the curing process to avoid resin starvation. At the maximum vacuum achievable void reduction is only to about 10%, with a higher applied vacuum resulting in larger void diameters. This paper reports on the use of high pressure of up to 7000 kPa by means of an isostatic press, without vacuum application, to effectively reduce the void levels to below 3%. Void reduction occurred only above an initial pressurization level, at about 400 kPa, after which significant reduction was obtained until about 5000 kPa. Further pressurization reduced the void content only marginally.