Clusterwise support vector linear regression

Joki, Kaisa; Bagirov, Adil M.; Karmitsa, Napsu; Mäkelä, Marko M.; Taheri, Sona

doi:10.1016/j.ejor.2020.04.032

Cited by 17 publications

(7 citation statements)

References 44 publications

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“…However, there are some successful exceptions. For example, abs-linear forms of prediction tasks are solved using a successive piecewise linearization method in [12,13], a primal-dual prox method for problems in which both the loss function and the regularizer are nonsmooth is developed in [14], various nonsmooth optimization methods are applied to solve clustering, classification, and regression problems in [5,6,[15][16][17], and finally, nonsmooth optimization approaches are combined with support vector machines in [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression

Karmitsa,

Taheri,

Joki

et al. 2023

Algorithms

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In this paper, a new nonsmooth optimization-based algorithm for solving large-scale regression problems is introduced. The regression problem is modeled as fully-connected feedforward neural networks with one hidden layer, piecewise linear activation, and the L1-loss functions. A modified version of the limited memory bundle method is applied to minimize this nonsmooth objective. In addition, a novel constructive approach for automated determination of the proper number of hidden nodes is developed. Finally, large real-world data sets are used to evaluate the proposed algorithm and to compare it with some state-of-the-art neural network algorithms for regression. The results demonstrate the superiority of the proposed algorithm as a predictive tool in most data sets used in numerical experiments.

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Section: Related Workmentioning

confidence: 99%

Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression

Karmitsa,

Taheri,

Joki

et al. 2023

Algorithms

Self Cite

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“…However, the value of the corresponding rule can be analyzed through a series of attributes of association rules. (1) Support representing the support of the union of the former and the latter items ( Joki et al, 2020 ); (2) Confidence involves the rules to identify the rule support/rule leader ( Lin and James, 2020 ); (3) Lift refers to the ratio of the probability of containing an left-hand side (LHS) and an right-hand side (RHS) to the probability of containing RHS ( Musab et al, 2019 ). It reflects the correlation between the LHS and the RHS in association rules.…”

Section: Specific Presentation Of Global Cyberspace Security Issuesmentioning

confidence: 99%

Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules

Tang

et al. 2021

Front. Psychol.

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This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection.

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“…Cluster regression was first mentioned by Späth [18], which has given rise to new ideas and vitality in the era of big data. Joki et al [19] introduced the support vector machine model in machine learning into CLR (Cluster-wise linear regression), transformed the problem into an unconstrained non-smooth optimization problem, and designed a method based on an incremental algorithm and double beam method combined with the DC optimization method. Numerical experiments verify the reliability and effectiveness of the method.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data

Wang

Qin

Dai

et al. 2023

IJERPH

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Clustering algorithms are widely used to mine the heterogeneity between meteorological observations. However, traditional applications suffer from information loss due to data processing and pay little attention to the interaction between meteorological indicators. In this paper, we combine the ideas of functional data analysis and clustering regression, and propose a functional clustering regression heterogeneity learning model (FCR-HL), which respects the data generation process of meteorological data while incorporating the interaction between meteorological indicators into the analysis of meteorological data heterogeneity. In addition, we provide an algorithm for FCR-HL to automatically select the number of clusters, which has good statistical properties. In the later empirical study based on PM2.5 concentrations and PM10 concentrations in China, we found that the interaction between PM10 and PM2.5 varies significantly between regions, showing several types of significant patterns, which provide meteorologists with new perspectives to further study the effects between meteorological indicators.

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Clusterwise support vector linear regression

Cited by 17 publications

References 44 publications

Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression

Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression

Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules

Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data

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