Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal

Graczyk, Magdalena; Lasota, Tadeusz; Trawiński, Bogdan; Trawiński, Krzysztof

doi:10.1007/978-3-642-12101-2_35

Cited by 103 publications

(61 citation statements)

References 20 publications

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“…In comparison with other ensemble methods, bagging ensemble is more consistent with the results [26]. Stacking ensemble method, as an example, returns highly variable results, in some cases increasing the prediction accuracy and in other ones giving an inaccurate prediction even if all models in the ensemble have high accuracy.…”

Section: Ensemble Methodssupporting

confidence: 64%

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Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Aceves-Fernández

Domínguez-Guevara

Pedraza-Ortega

et al. 2020

Discrete Dynamics in Nature and Society

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Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. is article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.Hindawi

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Section: Ensemble Methodssupporting

confidence: 64%

“…e main idea of ensemble methods is to determine a more precise prediction by means of the vote of diverse models. is has been studied in [25,26] where it was concluded that a more general model for prediction is obtained when an ensemble method is applied, but not in all applications.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Aceves-Fernández

Domínguez-Guevara

Pedraza-Ortega

et al. 2020

Discrete Dynamics in Nature and Society

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“…The misclassified examples are marked and their weights increased so they will have a higher probability of appearing in the training set of the next predictor. It results in different machines being specialized in predicting different areas of the dataset [8].…”

Section: Boostingmentioning

confidence: 99%

“…In the new dataset, each instance is related to the real value that it is suppose to predict. Then that dataset is used by stacking model learner (level-1) to provide the final output [8]. For example, the predicted classifications from the three base classifiers, naïve bayes, decision tree and rule induction can be used as input variables into a nearest neighbour classifier as a stacking model learner, which will attempt to learn from the data how to combine the predictions from the different models to achieve the best classification accuracy.…”

Section: Stackingmentioning

confidence: 99%

Application of Bagging, Boosting and Stacking to Intrusion Detection

Syarif

Zaluska

Prügel-Bennett

et al. 2012

Lecture Notes in Computer Science

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Abstract. This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and reduce the false positive rate. We use four different data mining algorithms, naïve bayes, J48 (decision tree), JRip (rule induction) and iBK( nearest neighbour), as base classifiers for those ensemble methods. Our experiment shows that the prototype which implements four base classifiers and three ensemble algorithms achieves an accuracy of more than 99% in detecting known intrusions, but failed to detect novel intrusions with the accuracy rates of around just 60%. The use of bagging, boosting and stacking is unable to significantly improve the accuracy. Stacking is the only method that was able to reduce the false positive rate by a significantly high amount (46.84%); unfortunately, this method has the longest execution time and so is insufficient to implement in the intrusion detection field.Keywords: Intrusion Detection System, bagging, boosting, stacking, ensemble classifiers Intrusion Detection SystemIntrusion detection is a process of gathering intrusion-related knowledge occurring in the process of monitoring events and analyzing them for signs of intrusion [1]. There are two basic IDS approaches: misuse detection (signature-based) and anomaly detection. The misuse detection system uses patterns of well-known attacks to match and identify known intrusions. It performs pattern matching between the captured network traffic and attack signatures. If a match is detected, the system generates an alarm. The main advantage of the signature detection paradigm is that it can accurately detect instances of known attacks. The main disadvantage is that it lacks the ability to detect new intrusions or zero-day attacks [16] [17]. The anomaly detection model works by identifying an attack by looking for behaviour that is out of the normal. It establishes a baseline model of behaviour for users and components in a computer or network. Deviations from the baseline cause alerts that direct the attention of human operators to the anomalies [17][18]. This system

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“…genetic fuzzy systems and artificial neural networks as both single models [7] and ensembles built using various resampling techniques [8], [9], [10], [11], [12], [13]. An especially good performance revealed evolving fuzzy models applied to cadastral data [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Neural Network Ensemble Approach to Predict from a Data Stream

Telec¹,

Trawiński²,

Lasota³

et al. 2014

Computational Collective Intelligence. Technologies and Applications

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Abstract.We have recently worked out a method for building reliable predictive models from a data stream of real estate transactions which applies the ensembles of genetic fuzzy systems and neural networks. The method consists in building models over the chunks of a data stream determined by a sliding time window and enlarging gradually an ensemble by models generated in the course of time. The aged models are utilized to compose ensembles and their output is updated with trend functions reflecting the changes of prices in the market. In the paper we present the next series of extensive experiments to evaluate our method with the ensembles of artificial neural networks. We examine the impact of the number of aged models used to compose an ensemble on the accuracy and the influence of the degree of polynomial trend functions employed to modify the results on the performance of neural network ensembles. The experimental results were analysed using statistical approach embracing nonparametric tests followed by post-hoc procedures designed for multiple N×N comparisons.

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Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal

Cited by 103 publications

References 20 publications

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Application of Bagging, Boosting and Stacking to Intrusion Detection

Evaluation of Neural Network Ensemble Approach to Predict from a Data Stream

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