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Solving Regression Problems Using Competitive Ensemble Models [chapter]

Yakov Frayman, Bernard F. Rolfe, Geoffrey I. Webb
2002 Lecture Notes in Computer Science  
The use of ensemble models in many problem domains has increased significantly in the last few years.  ...  A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression  ...  The selection model is solving a classification problem which is to choose the appropriate output of the one of the level 0 models as the final output of the ensemble.  ... 
doi:10.1007/3-540-36187-1_45 fatcat:rzs6gbolbrcspeqgpkbyk5gznq

An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning

Lijuan Yan, Yanshen Liu
2020 Symmetry  
We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes.  ...  The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.  ...  However, the general accuracy of logistic regression is not high and is easy to be underfitted so that it cannot deal with nonlinear features very well [31] .  ... 
doi:10.3390/sym12050728 fatcat:g3hd4tmy4rfxxpuoik7gsazaau

Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction [article]

Yujie Qian, Yinpeng Dong, Ye Ma, Hailong Jin, Juanzi Li
2016 arXiv   pre-print
We propose three ranking models and the ensemble methods for combining such models. Our experiment verifies the effectiveness of our approach.  ...  Measuring research impact and ranking academic achievement are important and challenging problems.  ...  MODEL In this section, we introduce three ranking models and the ensemble methods we used in the competition.  ... 
arXiv:1611.04369v1 fatcat:5s4qybjzdza7fam74xuid4x5ui

PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags [article]

Ji Ho Park, Peng Xu, Pascale Fung
2018 arXiv   pre-print
) and logistic regression to solve the competition tasks.  ...  We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR  ...  This vector representation then can be applied to machine learning models to solve problems like classification and regression.  ... 
arXiv:1804.08280v1 fatcat:zhizqilsajdnjhhxtjxmi2uqdy

PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags

Ji Ho Park, Peng Xu, Pascale Fung
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
) and logistic regression to solve the competition tasks.  ...  We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR  ...  This vector representation then can be applied to machine learning models to solve problems like classification and regression.  ... 
doi:10.18653/v1/s18-1039 dblp:conf/semeval/ParkXF18 fatcat:srznxfqdmjetjofwy5e3e4s3qu

An ensemble model with hierarchical decomposition and aggregation for highly scalable and robust classification

Quang Hieu Vu, Dymitr Ruta, Ling Cen
2017 Proceedings of the 2017 Federated Conference on Computer Science and Information Systems  
This paper introduces an ensemble model that solves the binary classification problem by incorporating the basic Logistic Regression with the two recent advanced paradigms: extreme gradient boosted decision  ...  We demonstrate the power of our ensemble model to solve the problem of predicting the outcome of Hearthstone, a turn-based computer game, based on game state information.  ...  In summary, our paper brings the following two main contributions. • An ensemble model that incorporates Logistic Regression, XGBoost and DL to solve the binary classification problem, along with the capability  ... 
doi:10.15439/2017f564 dblp:conf/fedcsis/VuRC17 fatcat:3aidylmjave7xfwg3tbt5ckvra

GPU-accelerated and parallelized ELM ensembles for large-scale regression

Mark van Heeswijk, Yoan Miche, Erkki Oja, Amaury Lendasse
2011 Neurocomputing  
The experiments show that competitive performance is obtained on the regression tasks, and that the GPU-accelerated and parallelized ELM ensemble achieves attractive speedups over using a single CPU.  ...  The paper presents an approach for performing regression on large data sets in reasonable time, using an ensemble of extreme learning machines (ELMs).  ...  Results of the experiments show competitive performance on the regression task, and validate our approach of using a GPUaccelerated and parallelized ensemble model of multiple ELMs: by adding more ELM  ... 
doi:10.1016/j.neucom.2010.11.034 fatcat:xt5n4cmio5hqdd7cv3jtrvedze

Overview of PAKDD Competition 2007

Junping Zhang, Guo-Zheng Li
2008 International Journal of Data Warehousing and Mining  
The PAKDD Competition 2007 involved the problem of predicting customers' propensity to take up a home loan when a collection of data from credit card users are provided.  ...  viewpoint of data preparation, resampling/reweighting and ensemble learning employed by different participants is given; and 3) Finally, some business insights are highlighted.  ...  regression models considered (PAKDD Competition, 2007) .  ... 
doi:10.4018/jdwm.2008040101 fatcat:o4ne37rcirdehbeyuqjuedqqge

Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems [article]

Hao Wang
2022 arXiv   pre-print
Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient.  ...  Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression.  ...  Regularization is a popular technique not only used in matrix factorization but also regression [8] [9] and ensemble tree models.  ... 
arXiv:2205.10492v1 fatcat:dx5cf26vtzfcdm54el4onzohmu

KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods [article]

Colleen M. Farrelly
2017 arXiv   pre-print
Specifically, these algorithms are tested on Tweedie regression problems through simulations and 6 real datasets; results are compared to state-of-the-art machine learning models including extreme learning  ...  Further, real dataset results suggest varying k is a good strategy in general (particularly for difficult Tweedie regression problems) and that KNN regression ensembles often outperform state-of-the-art  ...  K-nearest-neighbor (KNN) regression, a nonparametric regression method, has also been used to solve generalized linear modeling problems (Altman, 1992) , particularly for classification problems (Fuchs  ... 
arXiv:1708.02122v1 fatcat:7ngjdgcswvcbzh5vnjivkswtfu

Financial fraud detection by using Grammar-based multi-objective genetic programming with ensemble learning

Haibing Li, Man-Leung Wong
2015 2015 IEEE Congress on Evolutionary Computation (CEC)  
Third, it provides an efficient multi-objective method for solving FFD problems.  ...  Lastly, a new meta-heuristic approach is introduced for ensemble learning in order to help users to select patterns from a pool of models to facilitate final decision-making.  ...  Introduction to Data Mining Techniques Logistic Regression Logistic Regression (Shmueli et al., 2007) is one of the most widely used traditional techniques for solving data mining problems.  ... 
doi:10.1109/cec.2015.7257014 dblp:conf/cec/LiW15 fatcat:bn72akt7cbbu7dkpssyee63ac4

Bagging Model Trees for Classification Problems [chapter]

S. B. Kotsiantis, G. E. Tsekouras, P. E. Pintelas
2005 Lecture Notes in Computer Science  
In this study, model trees are coupled with bagging for solving classification problems.  ...  In order to apply this regression technique to classification problems, we consider the conditional class probability function and seek a model-tree approximation to it.  ...  In this study, model trees are coupled with bagging for solving classification problems.  ... 
doi:10.1007/11573036_31 fatcat:5vlzswxbpjhclhc5jocx6jjpxe

A Novel Ensemble Method for Regression via Classification Problems

Halawani
2011 Journal of Computer Science  
Experimental results suggest that the proposed ensembles perform competitively to the method developed specifically for regression problems.  ...  Results: We show that the proposed ensemble method is useful for RvC problems.  ...  classification models are used to solve the classification problems.  ... 
doi:10.3844/jcssp.2011.387.393 fatcat:udwpsvtylnbbtlnr55c53ba4ma

Data-Driven Model for Emotion Detection in Russian Texts

Alexander Sboev, Aleksandr Naumov, Roman Rybka
2021 Procedia Computer Science  
To conduct an emotion analysis, a method was created based on vector representations of words obtained by the ELMo language model, and subsequent processing by an ensemble classifier.  ...  To conduct an emotion analysis, a method was created based on vector representations of words obtained by the ELMo language model, and subsequent processing by an ensemble classifier.  ...  Acknowledgements The reported study was funded by an internal grant of the NRC "Kurchatov Institute" (Order No. 1359) and has been carried out using computing resources of the federal collective usage  ... 
doi:10.1016/j.procs.2021.06.075 fatcat:jddrpdvadfg2xoq36jncurgsiy

HAHA at FakeDeS 2021: A Fake News Detection Method Based on TF-IDF and Ensemble Machine Learning

Kun Li
2021 Annual Conference of the Spanish Society for Natural Language Processing  
We used five machine learning models, and achieved very competitive results on both the validation set and the test set, and got the second place in the final evaluation phase.  ...  Base on this task, we propose the classic TF-IDF feature extraction technology and Stacking ensemble learning method base on weak classifiers.  ...  We will solve this problem in future work.  ... 
dblp:conf/sepln/Li21a fatcat:54nkvi2355axrdk4wnciabc56a
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