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Efficient random forest Algorithm for multi-objective Optimization in Software Defect Prediction

IETE JOURNAL OF RESEARCH 2024年第4期70卷 3690-3702页

作者： Kanwar, Shailza Awasthi, Lalit Kumar Shrivastava, Vivek Natl Inst Technol Delhi Dept Comp Sci Engn Delhi India Natl Inst Technol Uttarakhand Dept Comp Sci Engn Srinagar India Natl Inst Technol Dept Elect Engn Delhi India

The Software Defect Prediction (SDP) process provides reliable software by identifying defect-prone modules before the testing stage. It efficiently and effectively utilizes quality assurance resources. Most predictive models are trained on historical data which belong to the same project or comparable project. These models show satisfactory performance as they utilize similar settings to the considered projects. But the limitation of these models is that they are effective only if there are adequate historical data to train a predictive model. In reality, however, such historical data are minimal for some projects and absent for new projects. The defect prediction in such projects which lack historical data can be accomplished by training prediction models on different project data. This process is known as Cross-Project Defect Prediction (CPDP). Software defect datasets also suffered from class imbalance issues which further degrades the model's performance. In this research work, the authors have proposed a multi-objective random forest (MO-RF) algorithm with a data resampling technique to minimize the probability of false alarms, to maximize the probability of detection and to overcome the class imbalance problem. The study also evaluates the performance of other prediction models. The proposed method has shown percentage improvement (in terms of AUC) of 2.78 and 3.46 over MONB and MONBNN, respectively.

关键词： Artificial intelligence Cross-project defect prediction Machine learning multi-objective random forest Optimization Software defect prediction

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Soil Heavy Metal Content Prediction Based on a Deep Belief Network and random forest Model

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APPLIED SPECTROSCOPY 2022年第9期76卷 1068-1079页

作者： Chen, Ying Liu, Zhengying Zhao, Xueliang Sun, Shicheng Li, Xiao Xu, Chongxuan Yanshan Univ Sch Elect Engn Hebei Prov Key Lab Test Measurement Technol & Ins Qinhuangdao Hebei Peoples R China Ctr Hydrogeol & Environm Geol China Geol Survey Geol Environm Monitoring Engn T Minist Nat Resources Baoding Peoples R China

In order to extract useful information from X-ray fluorescence (XRF) spectra and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combining a deep belief network (DBN) with a tree-based model was proposed. The DBN was first introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectra. Owing to the strong regression ability of the tree-based model, it can offset the deficiency of DBN in prediction ability so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R-2 of As and Pb reached 0.9884 and 0.9358, the mean square error of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results.

关键词： Deep belief network DBN multi-objective random forest sparrow search algorithm X-ray fluorescence analysis prediction model

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Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

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FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN multilayer feed-forward neural networks MLP multilayer perceptron BP Back Propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN Back propagation artificial neural network NN Neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA Gravitational search algorithm PSO Particle swarm optimization MPA Marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA Bayesian regularization algorithm MNN multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

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