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Journal articles on the topic 'Boosting and bagging'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Machova, Kristina, Miroslav Puszta, Frantisek Barcak, and Peter Bednar. "A comparison of the bagging and the boosting methods using the decision trees classifiers." Computer Science and Information Systems 3, no. 2 (2006): 57–72. http://dx.doi.org/10.2298/csis0602057m.

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In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of experiments with bagging and boosting methods. Our use of these methods aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging and boosting methods in connection with binary decision trees are presented. The minimum number of decision trees, which enables an improvement of the classification performed by the bagging and boosting methods, was found. The tests were carried out using the Reuter?s 21578 collection of documents as well as documents from an Internet portal of TV broadcasting company Mark?za. The comparison of our results on testing the bagging and boosting algorithms is presented.

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Taser, Pelin Yildirim. "Application of Bagging and Boosting Approaches Using Decision Tree-Based Algorithms in Diabetes Risk Prediction." Proceedings 74, no. 1 (March 4, 2021): 6. http://dx.doi.org/10.3390/proceedings2021074006.

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Diabetes is a serious condition that leads to high blood sugar and the prediction of this disease at an early stage is of great importance for reducing the risk of some significant diabetes complications. In this study, bagging and boosting approaches using six different decision tree-based (DTB) classifiers were implemented on experimental data for diabetes prediction. This paper also compares applied individual implementation, bagging, and boosting of DTB classifiers in terms of accuracy rates. The results indicate that the bagging and boosting approaches outperform the individual DTB classifiers, and real Adaptive Boosting (AdaBoost) and bagging using Naive Bayes Tree (NBTree) present the best accuracy score of 98.65%.

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Li, Xiao Bo. "Contrast Research of Two Kinds of Integrated Sorting Algorithms." Advanced Materials Research 433-440 (January 2012): 4025–31. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4025.

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Boosting and Bagging are two kinds of important voting sorting algorithms. Boosting algorithm can generate multiple classifiers by serialization through adjustment of sample weight; Bagging can generate multiple classifiers by parallelization. Different algorithms are composed of different loss and different integration mode, through integration of Bagging and Boosting algorithm and naïve Bayes algorithm, the Bagging NB and AdaBoost NB algorithms are constructed. Through experiment contrast of UCI data set, the result shows Bagging NB algorithm is relatively stable, it can produce the sorting result superior than that of NB algorithm, AdaBoost NB algorithm is greatly affected by the singular value in data distribution, the result with foundation of NB algorithm is relatively poor on part of data set, and that might have negative influence on the classifier algorithm.

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Ćwiklińska-Jurkowska, Małgorzata M. "Performance of Resampling Methods Based on Decision Trees, Parametric and Nonparametric Bayesian Classifiers for Three Medical Datasets." Studies in Logic, Grammar and Rhetoric 35, no. 1 (December 1, 2013): 71–86. http://dx.doi.org/10.2478/slgr-2013-0045.

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Abstract The figures visualizing single and combined classifiers coming from decision trees group and Bayesian parametric and nonparametric discriminant functions show the importance of diversity of bagging or boosting combined models and confirm some theoretical outcomes suggested by other authors. For the three medical sets examined, decision trees, as well as linear and quadratic discriminant functions are useful for bagging and boosting. Classifiers, which do not show an increasing tendency for resubstitution errors in subsequent boosting deterministic procedures loops, are not useful for fusion, e.g. kernel discriminant function. For the success of resampling classifiers’ fusion, the compromise be- tween accuracy and diversity is needed. Diversity important in the success of boosting and bagging may be assessed by concordance of base classifiers with the learning vector.

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Martínez-Muñoz, Gonzalo, and Alberto Suárez. "Using boosting to prune bagging ensembles." Pattern Recognition Letters 28, no. 1 (January 2007): 156–65. http://dx.doi.org/10.1016/j.patrec.2006.06.018.

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Anctil, F., and N. Lauzon. "Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions." Hydrology and Earth System Sciences 8, no. 5 (October 31, 2004): 940–58. http://dx.doi.org/10.5194/hess-8-940-2004.

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Abstract. Since the 1990s, neural networks have been applied to many studies in hydrology and water resources. Extensive reviews on neural network modelling have identified the major issues affecting modelling performance; one of the most important is generalisation, which refers to building models that can infer the behaviour of the system under study for conditions represented not only in the data employed for training and testing but also for those conditions not present in the data sets but inherent to the system. This work compares five generalisation approaches: stop training, Bayesian regularisation, stacking, bagging and boosting. All have been tested with neural networks in various scientific domains; stop training and stacking having been applied regularly in hydrology and water resources for some years, while Bayesian regularisation, bagging and boosting have been less common. The comparison is applied to streamflow modelling with multi-layer perceptron neural networks and the Levenberg-Marquardt algorithm as training procedure. Six catchments, with diverse hydrological behaviours, are employed as test cases to draw general conclusions and guidelines on the use of the generalisation techniques for practitioners in hydrology and water resources. All generalisation approaches provide improved performance compared with standard neural networks without generalisation. Stacking, bagging and boosting, which affect the construction of training sets, provide the best improvement from standard models, compared with stop-training and Bayesian regularisation, which regulate the training algorithm. Stacking performs better than the others although the benefit in performance is slight compared with bagging and boosting; furthermore, it is not consistent from one catchment to another. For a good combination of improvement and stability in modelling performance, the joint use of stop training or Bayesian regularisation with either bagging or boosting is recommended. Keywords: neural networks, generalisation, stacking, bagging, boosting, stop-training, Bayesian regularisation, streamflow modelling

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7

Sadorsky, Perry. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers." Journal of Risk and Financial Management 14, no. 5 (April 29, 2021): 198. http://dx.doi.org/10.3390/jrfm14050198.

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Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient boosting, and random forests produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for forecast horizons over 10 days. For those looking to forecast the direction of gold and silver prices, tree bagging and random forests offer an attractive combination of accuracy and ease of estimation. For each of gold and silver, a portfolio based on the random forests price direction forecasts outperformed a buy and hold portfolio.

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Akhand, M. A. H., Pintu Chandra Shill, and Kazuyuki Murase. "Hybrid Ensemble Construction with Selected Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 6 (August 20, 2011): 652–61. http://dx.doi.org/10.20965/jaciii.2011.p0652.

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A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.

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Arrahimi, Ahmad Rusadi, Muhammad Khairi Ihsan, Dwi Kartini, Mohammad Reza Faisal, and Fatma Indriani. "Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa." Jurnal Sains dan Informatika 5, no. 1 (July 14, 2019): 21–30. http://dx.doi.org/10.34128/jsi.v5i1.171.

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Undergraduate Students data in academic information systems always increases every year. Data collected can be processed using data mining to gain new knowledge. The author tries to mine undergraduate students data to classify the study period on time or not on time. The data is analyzed using CART with bagging techniqu, and CART with boosting technique. The classification results using 49 testing data, in the CART algorithm with bagging techniques 13 data (26.531%) entered into the classification on time and 36 data (73.469%) entered into the classification not on time. In the CART algorithm with boosting technique 16 data (32,653%) entered into the classification on time and 33 data (67,347%) entered into the classification not on time. The accuracy value of the classification of study period of undergraduate students using the CART algorithm is 79.592%, the CART algorithm with bagging technique is 81.633%, and the CART algorithm with boosting technique is 87.755%. In this study, the CART algorithm with boosting technique has the best accuracy value.

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Islam, M. M., Xin Yao, S. M. Shahriar Nirjon, M. A. Islam, and K. Murase. "Bagging and Boosting Negatively Correlated Neural Networks." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 3 (June 2008): 771–84. http://dx.doi.org/10.1109/tsmcb.2008.922055.

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Xiao, Tong, Jingbo Zhu, and Tongran Liu. "Bagging and Boosting statistical machine translation systems." Artificial Intelligence 195 (February 2013): 496–527. http://dx.doi.org/10.1016/j.artint.2012.11.005.

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12

Zhang, Chun-Xia, Jiang-She Zhang, and Gai-Ying Zhang. "Using Boosting to prune Double-Bagging ensembles." Computational Statistics & Data Analysis 53, no. 4 (February 2009): 1218–31. http://dx.doi.org/10.1016/j.csda.2008.10.040.

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13

Borra, Simone, and Agostino Di Ciaccio. "Improving nonparametric regression methods by bagging and boosting." Computational Statistics & Data Analysis 38, no. 4 (February 2002): 407–20. http://dx.doi.org/10.1016/s0167-9473(01)00068-8.

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Wang, Boyu, and Joelle Pineau. "Online Bagging and Boosting for Imbalanced Data Streams." IEEE Transactions on Knowledge and Data Engineering 28, no. 12 (December 1, 2016): 3353–66. http://dx.doi.org/10.1109/tkde.2016.2609424.

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Lemmens, Aurélie, and Christophe Croux. "Bagging and Boosting Classification Trees to Predict Churn." Journal of Marketing Research 43, no. 2 (May 2006): 276–86. http://dx.doi.org/10.1509/jmkr.43.2.276.

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16

Gweon, Hyukjun, Shu Li, and Rogemar Mamon. "AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS." ASTIN Bulletin 50, no. 3 (September 2020): 853–71. http://dx.doi.org/10.1017/asb.2020.28.

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AbstractTo evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.

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17

Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.

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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.

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Ghasemian, N., and M. Akhoondzadeh. "FUSION OF NON-THERMAL AND THERMAL SATELLITE IMAGES BY BOOSTED SVM CLASSIFIERS FOR CLOUD DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 83–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-83-2017.

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The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C) the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1.

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Subasi, Abdulhamit, Asalah Fllatah, Kholoud Alzobidi, Tayeb Brahimi, and Akila Sarirete. "Smartphone-Based Human Activity Recognition Using Bagging and Boosting." Procedia Computer Science 163 (2019): 54–61. http://dx.doi.org/10.1016/j.procs.2019.12.086.

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Ridgeway, Greg. "Looking for lumps: boosting and bagging for density estimation." Computational Statistics & Data Analysis 38, no. 4 (February 2002): 379–92. http://dx.doi.org/10.1016/s0167-9473(01)00066-4.

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Ditzler, Gregory, Joseph LaBarck, James Ritchie, Gail Rosen, and Robi Polikar. "Extensions to Online Feature Selection Using Bagging and Boosting." IEEE Transactions on Neural Networks and Learning Systems 29, no. 9 (September 2018): 4504–9. http://dx.doi.org/10.1109/tnnls.2017.2746107.

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Kotsiantis, Sotiris. "Combining bagging, boosting, rotation forest and random subspace methods." Artificial Intelligence Review 35, no. 3 (December 21, 2010): 223–40. http://dx.doi.org/10.1007/s10462-010-9192-8.

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Gupta, Surbhi, and Munish Kumar. "Forensic document examination system using boosting and bagging methodologies." Soft Computing 24, no. 7 (August 14, 2019): 5409–26. http://dx.doi.org/10.1007/s00500-019-04297-5.

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Tuysuzoglu, Goksu, and Derya Birant. "Enhanced Bagging (eBagging): A Novel Approach for Ensemble Learning." International Arab Journal of Information Technology 17, no. 4 (July 1, 2020): 515–28. http://dx.doi.org/10.34028/iajit/17/4/10.

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Bagging is one of the well-known ensemble learning methods, which combines several classifiers trained on different subsamples of the dataset. However, a drawback of bagging is its random selection, where the classification performance depends on chance to choose a suitable subset of training objects. This paper proposes a novel modified version of bagging, named enhanced Bagging (eBagging), which uses a new mechanism (error-based bootstrapping) when constructing training sets in order to cope with this problem. In the experimental setting, the proposed eBagging technique was tested on 33 well-known benchmark datasets and compared with both bagging, random forest and boosting techniques using well-known classification algorithms: Support Vector Machines (SVM), decision trees (C4.5), k-Nearest Neighbour (kNN) and Naive Bayes (NB). The results show that eBagging outperforms its counterparts by classifying the data points more accurately while reducing the training error

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Yaman, Emine, and Abdulhamit Subasi. "Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification." BioMed Research International 2019 (October 31, 2019): 1–13. http://dx.doi.org/10.1155/2019/9152506.

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The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers’ efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

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Kotsiantis, Sotiris B. "Bagging and boosting variants for handling classifications problems: a survey." Knowledge Engineering Review 29, no. 1 (August 23, 2013): 78–100. http://dx.doi.org/10.1017/s0269888913000313.

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AbstractBagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

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Bryndin, Evgeniy, and Irina Bryndina. "Communicative-Associative Transition to Smart Artificial Intelligence by Criteria with Help of Ensembles of Diversified Agents." Budapest International Research in Exact Sciences (BirEx) Journal 2, no. 4 (October 9, 2020): 418–35. http://dx.doi.org/10.33258/birex.v2i4.1256.

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Cognitive virtual smart artificial intelligence can be formed by ensembles of diversified agents with strong artificial intelligence based on communicative-associative logic by recurring development of professional skills, increasing visual, sound, and subject, spatial and temporal sensitivity. Several diversifiable agents that try to get the same conclusion will give a more accurate result, so several diversifiable agents are combined into an ensemble. Then, based on the criteria of utility and preference, the final result is obtained based on the conclusions of diversifying agents. This approach increases accuracy. Bagging and boosting techniques are used to form ensembles. Bagging is a combination of independent diversifiable agents by averaging patterns (weighted average, majority vote, or normal average). Boosting is the construction of ensembles of diversifiable agents consistently. The idea here is that the next agent will consider the errors of the previous agent. Due to the fact that diversifiable agents take into account errors committed by previous agents, it takes less time to get to a real response. The combination of Bagging and Boosting decision-making methods allows the development of intelligent artificial intelligence by ensembles of diversified agents. Cognitive virtual smart artificial intelligence becomes smarter through the accumulated professional experience of high-tech skills, competencies and knowledge, having increased visual, sound, subject, spatial and temporal sensitivity. Standardization of strong artificial intelligence and the use of ensembles of intelligent compatible diversified agents will help to find boundaries in which smart artificial intelligence will benefit humanity and not harm.

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Yılmaz Isıkhan, Selen, Erdem Karabulut, and Celal Reha Alpar. "Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data." Computational and Mathematical Methods in Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6794916.

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Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n=25 as a cutoff point for RT bagging to outperform a single RT.

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Małgorzata, Ćwiklińska-Jurkowska. "Boosting, Bagging and Fixed Fusion Methods Performance for Aiding Diagnosis." Biocybernetics and Biomedical Engineering 32, no. 2 (2012): 17–31. http://dx.doi.org/10.1016/s0208-5216(12)70034-7.

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Sawarn, Aman, Ankit, and Monika Gupta. "Comparative Analysis of Bagging and Boosting Algorithms for Sentiment Analysis." Procedia Computer Science 173 (2020): 210–15. http://dx.doi.org/10.1016/j.procs.2020.06.025.

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Khoshgoftaar, Taghi M., Jason Van Hulse, and Amri Napolitano. "Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, no. 3 (May 2011): 552–68. http://dx.doi.org/10.1109/tsmca.2010.2084081.

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Skurichina, Marina, and Robert P. W. Duin. "Bagging, Boosting and the Random Subspace Method for Linear Classifiers." Pattern Analysis & Applications 5, no. 2 (June 7, 2002): 121–35. http://dx.doi.org/10.1007/s100440200011.

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Nguyen, Kieu Anh, Walter Chen, Bor-Shiun Lin, and Uma Seeboonruang. "Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements." ISPRS International Journal of Geo-Information 10, no. 1 (January 19, 2021): 42. http://dx.doi.org/10.3390/ijgi10010042.

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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.

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Hou, Shuai, Fuan Hua, Wu Lv, Zhaodong Wang, Yujia Liu, and Guodong Wang. "Hybrid Modeling of Flotation Height in Air Flotation Oven Based on Selective Bagging Ensemble Method." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/281523.

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The accurate prediction of the flotation height is very necessary for the precise control of the air flotation oven process, therefore, avoiding the scratch and improving production quality. In this paper, a hybrid flotation height prediction model is developed. Firstly, a simplified mechanism model is introduced for capturing the main dynamic behavior of the process. Thereafter, for compensation of the modeling errors existing between actual system and mechanism model, an error compensation model which is established based on the proposed selective bagging ensemble method is proposed for boosting prediction accuracy. In the framework of the selective bagging ensemble method, negative correlation learning and genetic algorithm are imposed on bagging ensemble method for promoting cooperation property between based learners. As a result, a subset of base learners can be selected from the original bagging ensemble for composing a selective bagging ensemble which can outperform the original one in prediction accuracy with a compact ensemble size. Simulation results indicate that the proposed hybrid model has a better prediction performance in flotation height than other algorithms’ performance.

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Schwenk, Holger, and Yoshua Bengio. "Boosting Neural Networks." Neural Computation 12, no. 8 (August 1, 2000): 1869–87. http://dx.doi.org/10.1162/089976600300015178.

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Boosting is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm, Ada Boost, has been applied with great success to several benchmark machine learning problems using mainly decision trees as base classifiers. In this article we investigate whether Ada Boost also works as well with neural networks, and we discuss the advantages and drawbacks of different versions of the Ada Boost algorithm. In particular, we compare training methods based on sampling the training set and weighting the cost function. The results suggest that random resampling of the training data is not the main explanation of the success of the improvements brought by Ada Boost. This is in contrast to bagging, which directly aims at reducing variance and for which random resampling is essential to obtain the reduction in generalization error. Our system achieves about 1.4% error on a data set of on-line handwritten digits from more than 200 writers. A boosted multilayer network achieved 1.5% error on the UCI letters and 8.1% error on the UCI satellite data set, which is significantly better than boosted decision trees.

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Javidi, Mohammad Masoud. "Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies." Computer Engineering and Applications Journal 4, no. 1 (February 18, 2015): 61–81. http://dx.doi.org/10.18495/comengapp.v4i1.109.

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Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life. Such as, a movie can be categorized as action, crime, and thriller. Most algorithms on multi-label classification learning are designed for balanced data and donâ€™t work well on imbalanced data. On the other hand, in real applications, most datasets are imbalanced. Therefore, we focused to improve multi-label classification performance on imbalanced datasets. In this paper, a state-of-the-art multi-label classification algorithm, which called IBLR_ML, is employed. This algorithm is produced from combination of k-nearest neighbor and logistic regression algorithms. Logistic regression part of this algorithm is combined with two ensemble learning algorithms, Bagging and Boosting. My approach is called IB-ELR. In this paper, for the first time, the ensemble bagging method whit stable learning as the base learner and imbalanced data sets as the training data is examined. Finally, to evaluate the proposed methods; they are implemented in JAVA language. Experimental results show the effectiveness of proposed methods. Keywords: Multi-label classification, Imbalanced data set, Ensemble learning, Stable algorithm, Logistic regression, Bagging, Boosting

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A, Ramaswamyreddy, Shiva Prasad S, K. V. Rangarao, and A. Saranya. "Efficient datamining model for prediction of chronic kidney disease using wrapper methods." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 2 (April 20, 2019): 63. http://dx.doi.org/10.11591/ijict.v8i2.pp63-70.

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In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection. Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions. A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease. The capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.

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YUN, Yeboon, and Hirotaka NAKAYAMA. "1103 Effective Learning in Support Vector Machines using Bagging and Boosting." Proceedings of the Optimization Symposium 2014.11 (2014): _1103–1_—_1103–5_. http://dx.doi.org/10.1299/jsmeopt.2014.11.0__1103-1_.

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Abubacker, Nirase Fathima, Ibrahim Abaker Targio Hashem, and Lim Kun Hui. "Mammographic Classification Using Stacked Ensemble Learning with Bagging and Boosting Techniques." Journal of Medical and Biological Engineering 40, no. 6 (October 8, 2020): 908–16. http://dx.doi.org/10.1007/s40846-020-00567-y.

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Baumgartner, Dustin, and Gursel Serpen. "Performance of global–local hybrid ensemble versus boosting and bagging ensembles." International Journal of Machine Learning and Cybernetics 4, no. 4 (April 25, 2012): 301–17. http://dx.doi.org/10.1007/s13042-012-0094-8.

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Nai-Arun, Nongyao, and Punnee Sittidech. "Ensemble Learning Model for Diabetes Classification." Advanced Materials Research 931-932 (May 2014): 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1427.

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This paper proposed data mining techniques to improve efficiency and reliability in diabetes classification. The real data set collected from Sawanpracharak Regional Hospital, Thailand, was fist analyzed by using gain-ratio feature selection techniques. Three well known algorithms; naïve bayes, k-nearest neighbors and decision tree, were used to construct classification models on the selected features. Then, the popular ensemble learning; bagging and boosting were applied using the three base classifiers. The results revealed that the best model with the highest accuracy was bagging with base classifier decision tree algorithm (95.312%). The experiments also showed that ensemble classifier models performed better than the base classifiers alone.

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Goudman, Lisa, Jean-Pierre Van Buyten, Ann De Smedt, Iris Smet, Marieke Devos, Ali Jerjir, and Maarten Moens. "Predicting the Response of High Frequency Spinal Cord Stimulation in Patients with Failed Back Surgery Syndrome: A Retrospective Study with Machine Learning Techniques." Journal of Clinical Medicine 9, no. 12 (December 21, 2020): 4131. http://dx.doi.org/10.3390/jcm9124131.

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Despite the proven clinical value of spinal cord stimulation (SCS) for patients with failed back surgery syndrome (FBSS), factors related to a successful SCS outcome are not yet clearly understood. This study aimed to predict responders for high frequency SCS at 10 kHz (HF-10). Data before implantation and the last available data was extracted for 119 FBSS patients treated with HF-10 SCS. Correlations, logistic regression, linear discriminant analysis, classification and regression trees, random forest, bagging, and boosting were applied. Based on feature selection, trial pain relief, predominant pain location, and the number of previous surgeries were relevant factors for predicting pain relief. To predict responders with 50% pain relief, 58.33% accuracy was obtained with boosting, random forest and bagging. For predicting responders with 30% pain relief, 70.83% accuracy was obtained using logistic regression, linear discriminant analysis, boosting, and classification trees. For predicting pain medication decrease, accuracies above 80% were obtained using logistic regression and linear discriminant analysis. Several machine learning techniques were able to predict responders to HF-10 SCS with an acceptable accuracy. However, none of the techniques revealed a high accuracy. The inconsistent results regarding predictive factors in literature, combined with acceptable accuracy of the currently obtained models, might suggest that routinely collected baseline parameters from clinical practice are not sufficient to consistently predict the SCS response with a high accuracy in the long-term.

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Pal, Raj Kumar, Jugal Chaturvedi, V. Sai Teja, and Leena Shibu. "Applying Ensemble Approach on U.S. Census Data Classification." International Journal of Computer Science and Mobile Computing 10, no. 9 (September 30, 2021): 1–11. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.001.

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During this paper, we have a tendency to examine the adult financial gain dataset obtainable at the UC Irvine Machine Learning Repository. To aim predict whether or not associate individual’s financial gain are going to be bigger than $50,000 per annum victimization completely, different boosting and bagging strategies and compare models supported many attributes from the census information.

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Ramraj, S., S. Saranya, and K. Yashwant. "Comparative study of bagging, boosting and convolutional neural network for text classification." Indian Journal of Public Health Research & Development 9, no. 9 (2018): 1041. http://dx.doi.org/10.5958/0976-5506.2018.01138.5.

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Ng, Wing W. Y., Xiancheng Zhou, Xing Tian, Xizhao Wang, and Daniel S. Yeung. "Bagging–boosting-based semi-supervised multi-hashing with query-adaptive re-ranking." Neurocomputing 275 (January 2018): 916–23. http://dx.doi.org/10.1016/j.neucom.2017.09.042.

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Kuncheva, L. I., M. Skurichina, and R. P. W. Duin. "An experimental study on diversity for bagging and boosting with linear classifiers." Information Fusion 3, no. 4 (December 2002): 245–58. http://dx.doi.org/10.1016/s1566-2535(02)00093-3.

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Shrivastava, Santosh, P. Mary Jeyanthi, Sarbjit Singh, and David McMillan. "Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting." Cogent Economics & Finance 8, no. 1 (January 1, 2020): 1729569. http://dx.doi.org/10.1080/23322039.2020.1729569.

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Wang, Guan-Wei, Chun-Xia Zhang, and Gao Guo. "Investigating the Effect of Randomly Selected Feature Subsets on Bagging and Boosting." Communications in Statistics - Simulation and Computation 44, no. 3 (August 25, 2014): 636–46. http://dx.doi.org/10.1080/03610918.2013.788705.

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Shrestha, D. L., and D. P. Solomatine. "Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression." Neural Computation 18, no. 7 (July 2006): 1678–710. http://dx.doi.org/10.1162/neco.2006.18.7.1678.

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The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.

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Jurek, Anna, Yaxin Bi, Shengli Wu, and Chris Nugent. "A survey of commonly used ensemble-based classification techniques." Knowledge Engineering Review 29, no. 5 (May 3, 2013): 551–81. http://dx.doi.org/10.1017/s0269888913000155.

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AbstractThe combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition, we provide a review of different ensemble selection methods based on both static and dynamic approaches. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide a deeper insight into the ensembles themselves a range of existing theoretical studies have been reviewed in the paper.

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