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Medium- and long-term runoff forecasting based on a random forest regression model

WATER SUPPLY 2020年第8期20卷 3658-3664页

作者： Chen Shijun Wei Qin Zhu Yanmei Ma Guangwen Han Xiaoyan Wang Liang Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China State Grid Sichuan Elect Power Co Chengdu 610041 Peoples R China

Medium- and long-term runoff forecasting is closely related to the generation capacity forecasting of cascade hydropower stations, which is of great significance to power plants when arranging production plans and assisting market decisions. In order to improve the accuracy of runoff forecasting, an attempt was made to use random forest regression (RFR) to model the medium- and long-term runoff forecasting and to further make a verification based on the actual monthly runoff data of Mupo and Chuntangba stations. By comparison with the forecast results attained through a support vector machine (SVM) and an integrated autoregressive moving average model (IARMA), the results showed that the RFR model had the lowest mean square error (MSE) among the three methods. In addition, the coefficients of determination R-2 of the RFR for the two stations increased by 0.0261 and 0.0295 compared with the SVM model, and the R-2 rose by 0.1134 and 0.1332 compared with the IARMA model. The comparison of the three methods showed that the RFR had higher forecasting accuracy as well as stronger reliability and practicability than the IARMA model and the SVM model, so the RFR provided a new idea and method for the study of runoff forecasting.

关键词： bagging algorithm classification and regression tree mean square error random forest regression model runoff forecasting

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MLTs-ADCNs: Machine Learning Techniques for Anomaly Detection in Communication Networks

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IEEE ACCESS 2022年 10卷 91006-91017页

作者： Oleiwi, Haider W. Mhawi, Doaa N. Al-Raweshidy, Hamed Brunel Univ London Dept Elect & Elect Engn London UB8 3PH England Middle Tech Univ Dept Comp Syst Tech Baghdad 10010 Iraq

From a security perspective, the research of the jeopardized 6G wireless communications and its expected ultra-densified ubiquitous wireless networks urge the development of a robust intrusion detection system (IDS) with powerful capabilities which could not be sufficiently provided by the existing conventional systems. IDSs are still insufficient against continuous renewable unknown attacks on the wireless communication networks, especially with the new highly vulnerable networks, leading to low accuracy and detection rate with high (false-negative and false-positive) rates. To this end, this paper proposed a novel anomaly detection in communication networks by using an ensemble learning (EL) algorithm-based anomaly detection in communication networks (ADCNs). EL-ADCNs consists of four main stages;the first stage is the preprocessing steps. The feature selection method is the second stage. It adopts the proposed hybrid method using correlation with the random forest algorithm of ensemble learning (CFS-RF). It reduces dimensionality and retrieves the best subset feature of all the three datasets (NSL_KDD, UNSW_NB2015, and CIC_IDS2017) separately. The third stage is using hybrid EL algorithms to detect intrusions. It involves modifying two classifiers (i.e., random forest (RF), and support vector machine (SVM)) to apply them as adaboosting and bagging EL algorithms;using the voting average technique as an aggregation process. The final stage is testing the proposal using binary and multi-class classification forms. The experimental results of applying 30, 35, and 40 features of the proposed system to the three datasets achieved the best results of a 99.6% accuracy with a 0.004 false-alarm rate for NSL_KDD, a 99.1% accuracy with a 0.008 false-alarm rate for UNSW_NB2015, and a 99.4% accuracy with a 0.0012 false-alarm rate for CIC_IDS2017.

关键词： Feature extraction Classification algorithms Intrusion detection Correlation Support vector machines bagging Anomaly detection Machine learning Communication networks Adaboosting algorithm bagging algorithm correlation feature selection ensemble method intrusion detection systems

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Analysis on improving the application of machine learning in product development

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JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12435-12460页

作者： Chang, Yu-Teng Yang, Hui-Ru Chen, Chien-Ming Yu Da Univ Sci & Technol Dept Informat Management Miaoli 36143 Taiwan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

One of the main competitive advantages of an enterprise is its continuous development of new products. The higher the level of product innovation, the more the enterprise is ahead of its competitors. However, products that are more innovative have a higher chance of failure. Traditionally, enterprises use questionnaires, focus groups and customer interviews to understand the expectations and requirements of their customers for specific products;however, if the customers do not fully understand their own needs, they cannot express them fully. Therefore, in order to understand the functional requirements that are expected from their products, some enterprises go directly to the place where their customers are and observe how they use them. Nevertheless, the success rate for new product development is low. To solve this problem, this study uses the machine learning algorithm for the planning and prediction of product R&D. Firstly, the prediction model of a product's success rate is established by using the random tree algorithm, the bagging algorithm and the SMO algorithm. The model is then used for the planning and prediction of new products. The results show that the model accuracy of the random tree algorithm is 100%, that of the bagging algorithm is 83.33%, and that of the SMO algorithm is 91.67%. The prediction results of the three algorithms are integrated to judge the market's acceptance of the planned new products. Finally, the new products are improved, and after the improvements have been made, the three algorithms are used to make predictions. As a result, the acceptance of new products by customers has increased significantly. According to this study, machine learning can improve the success rate of new products in the marketplace.

关键词： Machine learning Product development Random tree algorithm bagging algorithm SMO algorithm

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A patient-independent classification system for onset detection of seizures

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第3期66卷 267-274页

作者： Ansari, Abdul Quaiyum Sharma, Priyanka Tripathi, Manjari Jamia Millia Islamia Dept Elect Engn Fac Engn & Technol New Delhi 110025 India All India Inst Med Sci AIIMS Neurosci Ctr Dept Neurol New Delhi India

Seizures are the most common brain dysfunction. Electroencephalography (EEG) is required for their detection and treatment initially. Studies show that if seizures are detected at their early stage, instant and effective treatment can be given to the patients. In this paper, an automated system for seizure onset detection is proposed. As the power spectrum of normal person's EEG and EEG of someone with epilepsy is plotted, powers present at different frequencies are found to be different for both. The proposed algorithm utilizes this frequency discrimination property of EEG with some statistical features to detect the seizure onset using simple linear classifier. The tests conducted on EEG data of 30 patients, obtained from the two different datasets, show the presence of all 183 seizures with mean latency of 0.9 s and 1.02 false detections per hour. The main contribution of this study is the use of simple features and classifier in the field of seizures onset detection that reduces the computational complexity of the algorithm. Also, the classifier used is patient independent. This patient independency in the classification system would be helpful in the implementation of the proposed algorithm to develop an online detection system.

关键词： bagging algorithm EEG epilepsy latency patient-independent classifier seizure onset detection

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Adaptive multi-time scale integration of the high-speed train fault samples

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第10期38卷 5229-5240页

作者： Liu, Suyan Wu, Chenxu Ma, Zengqiang Yuan, Zonghao Hebei Prov Collaborat Innovat Ctr Transportat Powe Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Struct Shijiazhuang 050043 Peoples R China Shijiazhuang Tiedao Univ Sch Traff & Transportat Shijiazhuang 050043 Peoples R China

High-quality fault samples are regarded as the foundation of data-driven high-speed train bearing fault diagnosis. An adaptive multi-time scale fault sample integration method based on long short-term memory network (LSTM) is proposed to address the problem of insufficient multi-time scale fault samples during the degradation of bearing performance. First, we used particle swarm optimization (PSO) to extract multiple hyper-parameters from the LSTM network and constructed the PSO-LSTM prediction models, which can predict fault samples. Thereafter, we generated high-accuracy prediction PSO-LSTM models based on multiple time scales. Finally, multiple PSO-LSTM prediction models are integrated by using the bagging algorithm. We compared the fault diagnostic rates of the integrated data model with the raw data using classic fault diagnosis algorithms. Experimental results indicated that the fault diagnostic rate of the integrated data is higher than that of the raw data.

关键词： bagging algorithm Fault diagnosis LSTM sample prediction Multi-time scale data integration

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Comparative analysis of learning and meta-learning algorithms for creating models for predicting the probable alcohol level during the ripening of grape berries

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2012年 80卷 54-62页

作者： Fernandez Martinez, Roberto Lostado Lorza, Ruben Fernandez Ceniceros, Julio Martinez-de-Pison Ascacibar, F. Javier Univ La Rioja EDMANS Grp Logrono Spain

The changes occurring in the dynamics of sugar concentration in grape berries are fairly significant during maturation, whereby they are commonly used as a marker of their development. In view of the importance this parameter has for wine producers, this paper designs several models for predicting the must's probable alcohol level using both meteorological variables and those specific to the vineyard. Presentation is made of a comparative analysis of learning and meta-learning algorithms for the selection of variables and the design of useful predictive models for estimating this level. The models are designed according to data gathered at different locations within the Rioja Qualified Designation of Origin (DOC Rioja, Spain) under different climate conditions, as well as involving different grape varieties. The models designed in this study provide very good results, and following their validation by experts, they have been proven to make a major contribution to decision-making in vine growing. Finally, considering the indices of analysis studied, it has been observed that the ensemble-type model based on the bagging algorithm with REPTree decision trees records the best results, with a root mean squared error (RMSE) of 8.1% and a correlation of 84.9%. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Grape sugar concentration Learning and meta-learning algorithms Feature selection Probable alcohol level bagging algorithm

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A study on the stratification of long-tail customers in civil aviation based on a cluster ensemble

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 5783-5799页

作者： Zong, Yi Li, Ying Pan, Enze Chen, Simin Zhang, Jingkuan Gao, Binbin Tianjin Univ Commerce Sch Management Tianjin Peoples R China Guangzhou Univ Sch Management Guangzhou Guangdong Peoples R China

Stratifying long-tail customers and identifying high-quality customers with high growth potential are crucial for civil aviation companies to explore new profit growth points. This paper proposes a long-tail customer stratification model based on clustering ensemble to address the problems of insufficient attention to long-tail customers in previous studies and the low accuracy and lack of accuracy testing of single clustering algorithms. First, the Bayesian information criterion is used to determine the optimal number of clusters. Then, an ensemble framework integrating the Gaussian mixture model, spectral clustering, Two step clustering and K-means algorithm is constructed, and the stacking and bagging ensemble methods are used for the cluster ensemble. Finally, three different indicators are used to evaluate the algorithm performance. Experimental results indicate that compared with single clustering algorithms, the Stacking algorithm increases the silhouette coefficient by 14.77% to 27.11%, the Calinski-Harabasz index by 38.83% to 122.18%, and the Davies-Bouldin Index by 19.38% to 98.04%. This indicates that each clustering has high cohesion and separation, with samples within a category being more closely related and those between categories having clear boundaries. It shows that the Stacking algorithm more accurately stratifies long-tail customers with similar consumption behaviors into different categories, achieving customer stratification.

关键词： Customer stratification long tail theory ensemble learning stacking algorithm bagging algorithm

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Improving Coronary Heart Disease Prediction Through Machine Learning and an Innovative Data Augmentation Technique

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COGNITIVE COMPUTATION 2023年第5期15卷 1687-1702页

作者： Al-Ssulami, Abdulrakeeb M. Alsorori, Randh S. Azmi, Aqil M. Aboalsamh, Hatim Taiz Univ Fac Appl Sci Dept Comp Sci Taizi Yemen King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia

Coronary heart disease (CHD) is a leading cause of death globally, with over 382,000 deaths in the USA alone in 2020. The early detection of CHD is critical in reducing mortality rates. Artificial intelligence (AI) is a constantly evolving field of computer science that employs computational models to extract insights from past data and provide rapid and accurate predictions for future cases. This paper presents a novel approach that generates an augmented dataset by selectively duplicating misclassified instances during the leave-one-out cross-validation (CV) process to overfit a model. We used a paired machine learning model with an augmented dataset approach to evaluate several classifiers. The comprehensive heart disease dataset [1] served as our base dataset. Our approach achieved higher accuracy than the base dataset, with the bagged decision tree (DT) algorithm outperforming state-of-the-art models and achieving an accuracy of 97.1% in the 10-fold CV test. Further experiments using the Cleveland dataset and the same 10-fold CV test resulted in an even higher accuracy of 99.2%. Combining an augmented dataset and the bagged-DT algorithm holds great promise for early CHD prediction helping reduce CHD mortality rates. The use of AI in early CHD prediction could potentially make a difference between the life and death of the patient.

关键词： Coronary heart disease bagging algorithm Decision tree Random forest Dataset augmentation

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Risk reduction for nonlinear prediction and its application to the surrogate data test

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PHYSICA D-NONLINEAR PHENOMENA 2014年 266卷 1-12页

作者： Suzuki, Tomoya Nakata, Kazuya Ibaraki Univ Coll Engn Dept Intelligent Syst Engn Hitachi Ibaraki 3168511 Japan

We propose a method for estimating nonlinear prediction risk using a bagging algorithm that involves ensemble learning. First we estimate the probability distribution of a future state as the ensemble set obtained using bagging predictors, and consider its standard deviation as the prediction risk. We can then improve the prediction reliability by avoiding dangerous predictions if the estimated prediction risk is high. As an application of this risk reduction method, we improve the power of surrogate data tests for system identification. Low prediction accuracy and poor system identification are caused by short and noisy data, so we perform simulations using short data derived from noisy chaotic models and real systems to confirm the validity of our method. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Nonlinear prediction Short data bagging algorithm Surrogate data test

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Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2018年第4期38卷 1004-1014页

作者： Goudarzi, Mahsa Maghooli, Keivan Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran

Mammography is an inexpensive and non-invasive method through which one can diagnose breast cancer in its early stages. As these images need interpretation by a radiologist, this may develop some problems due to fatigue, repetition, and need for a great deal of attention to details and other factors. Thus, a method capable of diagnosing breast cancer should be employed to help physicians in this regard. In this paper, The mini Mammographic Image Analysis Society (mini-MIAS) database of mammograms is used. The aim is to distinguish between normal and abnormal classes. In the preprocessing stage, noise removal, removal of labels of images, heightening the contrast, and ROI segmentation are performed, and then compactness, entropy, mean, and smoothness are extracted from the images. In addition to classification, we have come to a new approach in order to create a complete knowledge base, which then we use this knowledge base for classification. We have a comprehensive knowledge base which covers all the conceptual levels. The extracted features are referred to as fuzzy classifiers through the look-up table method. And, for evaluation of the results, the 10-fold method is used. Discretization operations are performed on training data across 2, 3, and 4 levels to develop concept hierarchy. Concept hierarchies reduce the data by replacing low-level concepts with higher-level concepts and the outcome is more meaningful and easier to interpret. Eventually, bagging algorithm is used for finding out the majority vote and the final result of the discretization levels. The obtained accuracy is 89.37 +/- 6.62. (C) 2018 Published by Elsevier B.V.

关键词： Mammography Breast cancer Feature extraction Fuzzy rule extraction Look-up table method bagging algorithm

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