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17th International Conference on Neural Information Processing

作者： Tang, Buzhou Chen, Qingcai Wang, Xuan Wang, Xiaolong Shenzhen Grad Sch Harbin Inst Technol Dept Comp Sci Shenzhen 518055 Peoples R China

ISBN: (纸本)9783642175367

Ensemble learning refers to the methods that combine multiple models to improve the performance. Ensemble methods, such as stacking, have been intensively studied, and can bring slight performance improvement. However, there is no guarantee that a stacking algorithm outperforms all base classifiers. In this paper, we propose a new stacking algorithm, where the predictive scores of each possible class label returned by the base classifiers are firstly collected by the meta-learner, and then all possible class labels are reranked according to the scores. This algorithm is able to find the best linear combination of the base classifiers on the training samples, which make sure it outperforms all base classifiers during training process. The experiments conducted on several public datasets show that the proposed algorithm outperforms the baseline algorithms and several state-of-the-art stacking algorithms.

关键词： ensemble learning stacking algorithm meta-learner

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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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RETRACTED: A hybrid model for text classification using part-of-speech features (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 1235-1249页

作者： Zou, Wang Zhang, Wubo Tian, Zhuofeng Wu, Wenhuan Hubei Univ Automot Technol Sch Elect & Informat Engn Shiyan Peoples R China Wuhan Univ Sci & Technol Sch Grammar & Econ Wuhan Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China

In the field of text classification, current research ignores the role of part-of-speech features, and the multi-channel model that can learn richer text information compared to a single model. Moreover, the method based on neural network models to achieve final classification, using fully connected layer and Softmax layer can be further improved and optimized. This paper proposes a hybrid model for text classification using part-of-speech features, namely PAGNN-stacking1. In the text representation stage of the model, introducing part-of-speech features facilitates a more accurate representation of text information. In the feature extraction stage of the model, using the multi-channel attention gated neural network model can fully learn the text information. In the text final classification stage of the model, this paper innovatively adopts stacking algorithm to improve the fully connected layer and Softmax layer, which fuses five machine learning algorithms as base classifier and uses fully connected layer Softmax layer as meta classifier. The experiments on the IMDB, SST-2, andAG News datasets show that the accuracy of the PAGNN-stacking model is significantly improved compared to the benchmark models.

关键词： Text classification part-of-speech features multi-channel stacking algorithm

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Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第10期40卷 3934-3942页

作者： Martin-Valdivia, Maria-Teresa Martinez-Camara, Eugenio Perea-Ortega, Jose-M Alfonso Urena-Lopez, L. Univ Jaen SINAI Res Grp Dept Comp Sci Jaen 23071 Spain

Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Sentiment polarity detection Multilingual opinion mining Spanish review corpus SentiWordNet Metaclassifiers stacking algorithm Voting system

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Variable segmentation and ensemble classifiers for predicting dairy cow behaviour

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BIOSYSTEMS ENGINEERING 2019年 178卷 156-167页

作者： Williams, Manod L. James, Wiliam P. Rose, Michael T. Aberystwyth Univ Inst Biol Environm & Rural Sci Penglais Campus Ceredigion SY23 3DA Wales

Automatically classifying cattle behaviour using high frequency data usually involves segmentation of data with fixed window sizes for feature extraction. Machine learning algorithms can then be used for supervised modelling of the most biologically important behaviours using these segments. In this work, variable segmentation was applied to GPS data gathered from 30 dairy cows at pasture. Using these segments, the performance of 13 machine learning algorithms (base learners) implemented in WEKA were compared using default parameters in classifying grazing, resting and walking. Two stacking ensembles (WEKA implementation of Super Learner) were then derived. The first ensemble contained the best performing base learners. The second was an optimised version derived using a manual ensemble selection method. Both versions of the ensemble were evaluated on an independent test set derived from 10 cows. Overall, the variable segmentation strategy identified 90.2% of changepoints. On the training set, all base learners achieved classification accuracies and F-measures >= 0.90. Optimising the stacking ensemble led to no further improvement in F-measure (full ensemble = 0.93;optimised ensemble = 0.92) on the test set. The ensembles performed well but base learners utilising boosting algorithms (e.g. simple logistic;logistic model trees) performed as well as the more computationally expensive ensembles. Variable segmentation and ensemble classifiers are promising strategies for classifying the behaviour of dairy cows. However, more work is needed to fully explore and evaluate the potential of ensembles because some base learners may perform equally if not better in some contexts. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Variable segmentation Changepoint Ensemble classifier Dairy cattle behaviour classification Machine learning stacking algorithm

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Integrating Spanish lexical resources by meta-classifiers for polarity classification

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JOURNAL OF INFORMATION SCIENCE 2014年第4期40卷 538-554页

作者： Martinez-Camara, Eugenio Teresa Martin-Valdivia, M. Dolores Molina-Gonzalez, M. Perea-Ortega, Jose M. Univ Jaen Dept Comp Sci Jaen 23071 Spain Commiss European Communities Joint Res Ctr Inst Protect & Secur Citizen I-21020 Ispra Italy

In this paper we focus on unsupervised sentiment analysis in Spanish. The lack of resources for languages other than English, as for example Spanish, adds more complexity to the task. However, we take advantage of some good already existing lexical resources. We have carried out several experiments using different unsupervised approaches in order to compare the different methodologies for solving the problem of the Spanish polarity classification in a corpus of movie reviews. Among all these approaches, perhaps the newest one integrates SentiWordNet with the Multilingual Central Repository to tackle polarity detection directly over the Spanish corpus. However, the results obtained were not as promising as we expected, and so we carried out another group of experiments combining all the methods using meta-classifiers. The results obtained with stacking outperformed the individual experiments and encourage us to continue in this way.

关键词： Lexical resources for opinion mining meta-classifiers Multilingual Central Repository SentiWordNet Spanish lexical resources stacking algorithm unsupervised polarity detection

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Prediction method for the porosity of tight sandstone constrained by lithofacies and logging resolution

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MARINE AND PETROLEUM GEOLOGY 2024年 170卷

作者： Zhao, Wenwen Zhang, Zhaohui Liao, Jianbo Zhang, Jianwu Zhang, Wenting Xinjiang Univ Sch Geol & Min Engn Urumqi 830046 Peoples R China Xinjiang Key Lab Geodynam Proc & Metallogen Progno Urumqi 830046 Peoples R China PetroChina Res Inst Petr Explorat & Dev Northwest Branch Lanzhou 730020 Peoples R China PetroChina Changqing Oilfield Branch Co Res Inst Explorat & Dev Xian 710018 Peoples R China

The resource potential of the Upper Triassic Yanchang Formation in the Ordos Basin is considerably large. However, owing to the low porosity and permeability, poor connectivity, and strong heterogeneity of tight sandstone, predicting the porosity of tight sandstone in this formation poses substantial challenges. The lithology of Chang 71 1 sub-member is dominated by arkose and lithic arkose, wherein the porosities mainly ranged between 3 and 13% and pore types were mainly feldspar dissolution pores. Based on core sample experiments and thin section analysis, combined with conventional logging curves, we proposed a three-porosity weighted average prediction method under lithological control, and a novel method for reading logging curves controlled by logging resolution is developed. We used principal component analysis to optimise the logging curves and reduce the impact of complex coupling relationships between logging curves on lithofacies identification. The stacking algorithm, which is a combination of the random forest and extreme gradient boosting models, was applied to divide the lithofacies into five categories: homogeneous-distributary-channel fine sandstone, heterogeneous- distributary-channel fine sandstone, homogeneous-mouth-bar fine sandstone, heterogeneous-mouth-bar fine sandstone, and shallow lacustrine mudstone. Compared with the results of manual classification based on logs, the accuracy of lithofacies recognition was approximately 94.2%. Additionally, sensitivity analysis of porosity curves was conducted on four types of lithofacies (i.e., except for shallow lacustrine mudstone), and porosity models for each lithofacies were established, providing an effective and objective method for the accurate prediction of porosity. This prediction method comprehensively considers sedimentary factors and incorporates statistics that are fitted using multiple linear regression, which is highly reliable. In the Chang 71 1 sub-member, the fitting degree between t

关键词： Lithofacies-constrained stacking algorithm Porosity prediction Tight sandstone Ordos basin

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A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques

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INTERNATIONAL JOURNAL OF LEGAL MEDICINE 2023年第1期137卷 237-249页

作者： Lu, Xiao-jun Li, Jian Wei, Xue Li, Na Dang, Li-hong An, Guo-shuai Du, Qiu-xiang Jin, Qian-qian Cao, Jie Wang, Ying-yuan Sun, Jun-hong Shanxi Med Univ Sch Forens Med 98 Univ St Jinzhong 030604 Shanxi Peoples R China Baotou Publ Secur Bur Criminal Invest Detachment 191 Jianshe Rd Baotou City 014030 Inner Mongolia Peoples R China

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

关键词： Forensic pathology Metabolomics Machine learning algorithms Postmortem interval stacking algorithm Classification

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A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments

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INTERNATIONAL JOURNAL OF LEGAL MEDICINE 2024年第3期138卷 1093-1107页

作者： Shen, Zefang Zhong, Yue Wang, Yucong Zhu, Haibiao Liu, Ran Yu, Shengnan Zhang, Haidong Wang, Min Yang, Tiantong Zhang, Mengzhou China Univ Polit Sci & Law Key Lab Evidence Sci Minist Educ 25 Xitucheng Rd Beijing 100088 Peoples R China Forens Sci Ctr Beijing Huatong Junjian Sci & Techn Beijing 100016 Peoples R China

The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.

关键词： Forensic pathology Postmortem computed tomography Machine learning algorithms Postmortem interval stacking algorithm

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Texture recognition of pulmonary nodules based on volume local direction ternary pattern

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BIOENGINEERED 2020年第1期11卷 904-920页

作者： Fan, Zhipeng Sun, Huadong Ren, Cong Han, Xiaowei Zhao, Zhijie Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Peoples R China Key Lab Elect Commerce & Informat Proc Heilongjia Harbin Heilongjiang Peoples R China

In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what's good and what's bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis.

关键词： CAD random walk volume local direction ternary pattern stacking algorithm

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