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Boosted Fuzzy Granular Regression Trees

Wei Li, Youmeng Luo, Chao Tang, Kaiqiang Zhang, Xiaoyu Ma, Petr Hájek
2021 Mathematical Problems in Engineering  
Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a  ...  Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented.  ...  granular regression trees as the final regression value.  ... 
doi:10.1155/2021/9958427 fatcat:4ni3ysxau5e3vp5fjbxwriumaq

Granular Elastic Network Regression with Stochastic Gradient Descent

Linjie He, Yumin Chen, Caiming Zhong, Keshou Wu
2022 Mathematics  
Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model.  ...  We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model.  ...  We will propose a new granulation approach to apply granular regression to big data. In future work, we will apply the granular regression system to the nonlinear regression system.  ... 
doi:10.3390/math10152628 fatcat:36luseab5fa3jiey5divsmopqi

Genetic interval neural networks for granular data regression

Mario G.C.A. Cimino, Beatrice Lazzerini, Francesco Marcelloni, Witold Pedrycz
2014 Information Sciences  
Granular data and granular models offer an interesting tool for representing data in problems involving uncertainty, inaccuracy, variability and subjectivity have to be taken into account.  ...  The proposed MLP comes with interval-valued weights and biases, and is trained using a genetic algorithm designed to fit data with different levels of granularity.  ...  Our focus is on an interval regression problem. The first conceptualization of neural networks for processing granular data was introduced by Pedrycz and Vukovich [28] .  ... 
doi:10.1016/j.ins.2012.12.049 fatcat:nje3e742m5fnffv5slqemowfx4

Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification

Valerio Modugno, Francesca Possemato, Antonello Rizzi
2014 Proceedings of the International Conference on Evolutionary Computation Theory and Applications  
In this paper we propose a new method for a trend prediction on financial time series combining a Linear Piecewise Regression with a granular computing framework.  ...  In this paper we introduce a new Hybrid Classification Algorithm based on a Piecewise Linear Regression (PLR) preprocessing of raw data within a Granular Computing (GrC) framework (Bargiela and Pedrycz  ...  Linear Piecewise Regression First we collect raw data about the prices of a stock, in a fixed time interval, from a specific data file.  ... 
doi:10.5220/0005127402810288 dblp:conf/ijcci/ModugnoPR14 fatcat:ynwfklj5gjgq5bti4o77v4572q

Spontaneous Regression of Methotrexate-related Lymphoproliferative Disorder with T-cell Large Granular Lymphocytosis

Hiroshi Ureshino, Chiho Kadota, Kazuya Kurogi, Masaharu Miyahara, Shinya Kimura
2015 Internal medicine (Tokyo. 1992)  
We herein show that spontaneous regression of MTX-LPDs is associated with the development of significant and transient T-cell large granular lymphocyte (T-LGL) lymphocytosis induced by MTX withdrawal.  ...  Since T-LGLs show strong cytotoxicity, their expansion may contribute to the spontaneous regression of lymphoma.  ...  regression (10) .  ... 
doi:10.2169/internalmedicine.54.4207 pmid:26328653 fatcat:lpab2ntwvvaopgld2l7f43rmcq

Adaptive and iterative least squares support vector regression based on quadratic Renyi entropy

Jingqing Jiang, Chuyi Song, Haiyan Zhao, Chunguo Wu, Yanchun Liang
2008 2008 IEEE International Conference on Granular Computing  
The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved.  ...  i e b x w y i i T i ,..., 1 , ) ( = + + = ϕ (3) This corresponds to a form of ridge regression.  ...  Calculate α and b according to Eq.(8) and obtain the regression function W x f | ) ( .  ... 
doi:10.1109/grc.2008.4664732 dblp:conf/grc/JiangSZWL08 fatcat:cmmhiw7fbjgwtpi2cdtgvtgywm

Support Vector Regression Modelling of an Aerobic Granular Sludge in Sequential Batch Reactor

Nur Sakinah Ahmad Yasmin, Norhaliza Abdul Wahab, Fatimah Sham Ismail, Mu'azu Jibrin Musa, Mohd Hakim Ab Halim, Aznah Nor Anuar
2021 Membranes  
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures.  ...  The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment  ...  The biological treatment of this study is based on aerobic granular sludge (AGS) instead of conventional activated sludge systems.  ... 
doi:10.3390/membranes11080554 pmid:34436317 pmcid:PMC8400290 fatcat:uc5zwncn4rccpphrlh4gpxg3pi

The impact of test suite granularity on the cost-effectiveness of regression testing

Gregg Rothermel, Sebastian Elbaum, Alexey Malishevsky, Praveen Kallakuri, Brian Davia
2002 Proceedings of the 24th international conference on Software engineering - ICSE '02  
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases  ...  Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites.  ...  Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques.  ... 
doi:10.1145/581339.581358 dblp:conf/icse/RothermelEMKD02 fatcat:uiwle23u3vbg3dxbsccippfnou

The impact of test suite granularity on the cost-effectiveness of regression testing

Gregg Rothermel, Sebastian Elbaum, Alexey Malishevsky, Praveen Kallakuri, Brian Davia
2002 Proceedings of the 24th international conference on Software engineering - ICSE '02  
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases  ...  Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites.  ...  Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques.  ... 
doi:10.1145/581356.581358 fatcat:sslwcpmj2nbkzd7kxqab5iejiq

RFIMiner: A regression-based algorithm for recently frequent patterns in multiple time granularity data streams

Lifeng Jia, Zhe Wang, Nan Lu, Xiujuan Xu, Dongbin Zhou, Yan Wang
2007 Applied Mathematics and Computation  
First, a regression-based data stream model is proposed to differentiate new and old transactions.  ...  FP-stream employs a novel titled-time windows technique and mines frequent itemsets at multiple time granularities.  ...  Therefore, new transactions are always in a smaller and finer time granularity than old ones. Again, transferences between two time granularities cause the regression of transactions.  ... 
doi:10.1016/j.amc.2006.06.115 fatcat:45kiaxjat5dsxgm6wne6gzpa4u

Adaptive and iterative least squares support vector regression based on quadratic Renyi entropy

Jingqing Jiang, Chuyi Song, Haiyan Zhao, Chunguo Wu, Yanchun Liang
2010 International Journal of Granular Computing Rough Sets and Intelligent Systems  
The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved.  ...  i e b x w y i i T i ,..., 1 , ) ( = + + = ϕ (3) This corresponds to a form of ridge regression.  ...  Calculate α and b according to Eq.(8) and obtain the regression function W x f | ) ( .  ... 
doi:10.1504/ijgcrsis.2010.029579 fatcat:zjv2cjgqzzgrhnsth7w6n5whc4

Practical Validity of the Continuity Assumption in Regression Discontinuity Designs: Granularity, Degree of Focus, and Multiple Thresholds

Dominic Albino
2020 unpublished
"Regression discontinuity analysis: an alternative to the ex-post facto experiment." Journal of Educational Psychology, 51:309-317.  ...  I discuss how the number of problematic, untestable variables can vary according to granularity of the data and degree of focus at the discontinuity threshold and suggest possible manipulations, including  ...  For an overview of the principles of regression discontinuity design, see Hahn et al. (2001) and for a more complete guide to common use, see Lee and Lemieux (2010) .  ... 
doi:10.13140/rg.2.2.22163.02082 fatcat:3yacmway6jd2lp56ns2td67b6a

The impact of test suite granularity on the cost-effectiveness of regression testing

G. Rothermel, S. Elbaum, A. Malishevsky, P. Kallakuri, B. Davia
Proceedings of the 24th International Conference on Software Engineering. ICSE 2002  
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases  ...  Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites.  ...  Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques.  ... 
doi:10.1109/icse.2002.1007962 fatcat:sixdbbwz3ja3hoci7lbd64t3qa

Successive Overrelaxation for Support Vector Regression [chapter]

Yong Quan, Jie Yang, Chenzhou Ye
Lecture Notes in Computer Science  
Training a SVR (support vector regression) requires the solution of a very large QP (quadratic programming) optimization problem.  ...  In this work, we propose a new way to make SVR (support vector regression) have the similar mathematic form as that of a support vector classification, and derive a generalization of SOR to handle regression  ...  Because of these constraints, Shevade [6] and Chih-Jen [7] generalized SMO so that it can handle regression problems.  ... 
doi:10.1007/3-540-39205-x_109 dblp:conf/rsfdgrc/QuanYY03 fatcat:35k6a4rf3ndxdej3qrj7xjhdoe

Page 932 of SMPTE Motion Imaging Journal Vol. 92, Issue 9 [page]

1983 SMPTE Motion Imaging Journal  
Also, the re- gression analysis of the perceived graininess on the combined blue-, green-, and red-filter RMS-granular- T of Psy. Sc. Gr. Model coefficient F of regression R? of regression pt?  ...  According to Figs. 5, 6a, and 7 and Chart 8, the visually perceived grain- Chart 8 — Regression Analysis iness is apparently largely determined by the granularity of the magenta and the cyan layer and  ... 
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