scholarly journals An overview of regression techniques for knowledge discovery

1999 ◽  
Vol 14 (4) ◽  
pp. 319-340 ◽  
Author(s):  
İLHAN UYSAL ◽  
H. ALTAY GÜVENIR
Keyword(s):  
Recursive Partitioning ◽  
Real Life ◽  
Projection Pursuit ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Methods ◽  
Life Problems ◽  
Adaptive Regression ◽  
Regression Techniques ◽  
Regression Problems ◽  
Adaptive Regression Splines

Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).

scholarly journals Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

2018 ◽  
Vol 210 ◽  
pp. 02016 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Supervised Machine Learning ◽  
Electrical Tomography ◽  
Regression Error ◽  
Adaptive Regression ◽  
Regression Problems ◽  
Adaptive Regression Splines

The article presents four selected methods of supervised machine learning, which can be successfully used in the tomography of flood embankments, walls, tanks, reactors and pipes. A comparison of the following methods was made: Artificial Neural Networks (ANN), Supported Vector Machine (SVM), K-Nearest Neighbour (KNN) and Multivariate Adaptive Regression Splines (MAR Splines). All analysed methods concerned regression problems. Thanks to performed analysis the differences expressed quantitatively were visualized with the use of indicators such as regression, error of mean square deviation, etc. Moreover, an innovative method of denoising tomographic output images with the use of convolutional auto-encoders was presented. Thanks to the use of a convolutional structure composed of two auto-encoders, a significant improvement in the quality of the output image from the ECT tomography was achieved.

scholarly journals Multivariate Adaptive Regression Splines pada Kasus Inflasi di Indonesia Tahun 2005-2018

2020 ◽  
Vol 2 (3) ◽  
pp. 110
Author(s):  
Nurhaerunisa Widagdo ◽  
Muhammad Kasim Aidid ◽  
S. Sudarmin
Keyword(s):  
Cross Validation ◽  
Recursive Partitioning ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Generalized Cross Validation ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Abstrak. Kegiatan perekonomian suatu negara dipengaruhi oleh inflasi yang terjadi pada negara tersebut. Tingkat inflasi Indonesia yang fluktuatif, cenderung tidak stabil, mempengaruhi kehidupan sosial dan ekonomi masyarakat. Sehingga penting untuk mengetahui faktor-faktor yang berpengaruh terhadap inflasi serta pemodelan faktor-faktor berpengaruh tersebut dan hubungannya terhadap inflasi. Mengidentifikasi hubungan inflasi dan faktor penyebabnya dilakukan menggunakan pemodelan Multivariate Adaptive Regression Splines (MARS). MARS merupakan jenis regeresi nonparametrik yang menggabungkan prinsip Recursive Partitioning Regression (RPR) dan spline, fleksibel dalam memodelkan data sehingga memberikan hasil pemodelan data yang cukup akurat serta dapat menangani data berdimensi tinggi, yaitu data dengan jumlah peubah prediktor 3 ≤ x ≤ 20 dan ukuran data sampel 50 ≤ n ≤ 1000. Model MARS diperoleh berdasarkan kombinasi nilai BF, MI, dan MO yang memiliki nilai Generalized Cross Validation (GCV) terkecil. Pada penelitian ini digunakan enam peubah prediktor sebagai faktor yang mempengaruhi inflasi dengan data sampel sebesar 168 sampel. Hasil penelitian menunjukkan bahwa peubah Indeks Harga Perdagangan Besar (IHPB), BI Rate, Nilai Tukar IDR-USD, dan Uang Beredar adalah faktor-faktor yang berpengaruh terhadap inflasi berdasarkan model terbaik MARS dengan BF=24, MI=3, MO=1, GCV=0,772, MSE=0,391, dan R2=0,968.Kata kunci: Inflasi, MARS, RPR, BF, MI, MO, GCV.

scholarly journals Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 62
Author(s):  
Snezhana Gocheva-Ilieva ◽  
Hristina Kulina ◽  
Atanas Ivanov
Keyword(s):  
Goodness Of Fit ◽  
Applied Mathematics ◽  
Multivariate Adaptive Regression Splines ◽  
Actual Data ◽  
Final Exam ◽  
Regression Methods ◽  
Adaptive Regression ◽  
Competing Models ◽  
Adaptive Regression Splines ◽  
The Impact

The aim of this study is to evaluate students’ achievements in mathematics using three machine learning regression methods: classification and regression trees (CART), CART ensembles and bagging (CART-EB) and multivariate adaptive regression splines (MARS). A novel ensemble methodology is proposed based on the combination of CART and CART-EB models in a new ensemble to regress the actual data using MARS. Results of a final exam test, control and home assignments, and other learning activities to assess students’ knowledge and competencies in applied mathematics are examined. The exam test combines problems on elements of mathematical analysis, statistics and a small practical project. The project is the new competence-oriented element, which requires students to formulate problems themselves, to choose different solutions and to use or not use specialized software. Initially, empirical data are statistically modeled using six CART and six CART-EB competing models. The models achieve a goodness-of-fit up to 96% to actual data. The impact of the examined factors on the students’ success at the final exam is determined. Using the best of these models and proposed novel ensemble procedure, final MARS models are built that outperform the other models for predicting the achievements of students in applied mathematics.

Prediction of transportation energy demand: Multivariate Adaptive Regression Splines

Energy ◽  
2021 ◽  
Vol 224 ◽  
pp. 120090
Author(s):  
Mohammad Ali Sahraei ◽  
Hakan Duman ◽  
Muhammed Yasin Çodur ◽  
Ecevit Eyduran
Keyword(s):  
Energy Demand ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Transportation Energy ◽  
Adaptive Regression ◽  
Adaptive Regression Splines
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