A novel Boosted-neural network ensemble for modeling multi-target regression problems

Hadavandi, Esmaeil; Shahrabi, Jamal; Shamshirband, Shahaboddin

doi:10.1016/j.engappai.2015.06.022

Cited by 43 publications

(15 citation statements)

References 42 publications

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“…The majority rule is the most widely used fusion rule for ensemble learning since the earliest studies of the subject (Blum 1995;Breiman 1996;Canzian et al 2013;Fan et al 1999;Freund and Schapire 1997;Hadavandi et al 2015;Herbster and Warmuth 1998;Littlestone and Warmuth 1994;Schapire 1990;Stahl et al 2015;Wang et al 2003Wang et al , 2015. In majority rule, the combiner's final decision is made by taking a vote among all the experts at each instant.…”

Section: Numerical Resultsmentioning

confidence: 99%

Context-based unsupervised ensemble learning and feature ranking

2016

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In ensemble systems, several experts, which may have access to possibly different data, make decisions which are then fused by a combiner (meta-learner) to obtain a final result. Such ensemble-based systems are well-suited for processing big-data from sources such as social media, in-stream monitoring systems, networks, and markets, and provide more accurate results than single expert systems. However, most existing ensemble-learning techniques have two limitations: (i) they are supervised, and hence they require access to the true label, which is often unknown in practice, and (ii) they are not able to evaluate the impact of the various data features/contexts on the final decision, and hence they do not learn which data is required. In this paper we propose a joint estimation-detection method for evaluating the accuracy of each expert as a function of the data features/context and for fusing the experts decisions. The proposed method is unsupervised: the true labels are not available and no prior information is assumed regarding the performance of each expert. Extensive simulation results show the improvement of the proposed method as compared to the state-of-the-art approaches. We also provide a systematic, unsupervised method for ranking the informativeness of each feature on the decision making process.

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Section: Numerical Resultsmentioning

confidence: 99%

Context-based unsupervised ensemble learning and feature ranking

2016

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“…Computational intelligence methods such as an artificial neural network (ANNs) [129] are modern paradigms to handle complex optimization problems [130][131][132]. ANN is organized as a simplified abstract of the biological nervous system to emulate neurons mechanism.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…An ANN consists of characteristics: the input layer, the hidden layer, the interconnection between different layers, the learning step to find the optimum values of interconnections weights, the transformer function which assigned to produce outputs refer to weighted inputs, the number of neurons performing in each layer and the output layer. Computational intelligence methods such as an artificial neural network (ANNs) [129] are modern paradigms to handle complex optimization problems [130][131][132]. ANN is organized as a simplified abstract of the biological nervous system to emulate neurons mechanism.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

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“…In other words, MI provides a decision-making system that can be used to select the most effective inputs (variables which can represent the influence of other ones) and reduce the noises for the development of a model (Chelgani et al 2018). In a predictive modeling problem, various researches indicated that combination of intelligent predictor models and development of an ensemble of predictors (experts) can construct an accurate model to deal with complicated problems (Masoudnia et al 2012;Hadavandi et al 2015Hadavandi et al , 2016. One of the popular ensemble methods is the neural network ensemble (NNE) (Hansen and Salamon 1990) and an efficient approach for creating an NNE model is Adaptive Boosting (Adaboost) that can adaptively improve the probability of sampling cases for accurate training experts for the NNE model.…”

Section: Introductionmentioning

confidence: 99%

Exploring relationships between mechanical properties of marl core samples by a coupling of mutual information and predictive ensemble model

Salehin

Hadavandi

Chelgani

2019

Model. Earth Syst. Environ.

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Inappropriate evaluation of uniaxial compression indexes (E and UCS) of rocks in high seismic intensity areas such as dam regions can lead to underestimation of the load, and possible settlement of the structure. Indirect assessments of these rock mechanical indexes based on non-destructive experiments and by using intelligent models is a well-accepted method to overcome associated limitations with laboratory tests of E and UCS. This study introduces the mutual information (MI) method as a unique system for variable importance measurement (VIM) and feature selection. Conducting MI-VIM assessments between various analyses of marl core samples (depth, density, ultrasonic tests (ν d , V p and V s), Brazilian test (σt), triaxial compression test (C and and ϕ) and point load test (I s (50)) indicated that V s and σt had the highest importance for E and UCS prediction. adaptive boosting-neural network ensemble (Adaboost-NNE) was used for the prediction of E and UCS. Testing of the generated Adaboost-NNE indicated that this model could accurately predict UCS and E with correlations of determinations 0.98 and 0.92, respectively. These results showed that VIM of MI coupled with Adaboost-NNE could develop a robust model that can be used for the prediction and modeling of other indexes of rocks.

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A novel Boosted-neural network ensemble for modeling multi-target regression problems

Cited by 43 publications

References 42 publications

Context-based unsupervised ensemble learning and feature ranking

Context-based unsupervised ensemble learning and feature ranking

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Exploring relationships between mechanical properties of marl core samples by a coupling of mutual information and predictive ensemble model

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