scholarly journals Otomobil sahipliğini etkileyen faktörlerin farklı regresyon modelleri ile incelenmesi

2021 ◽  
Vol 2 (2) ◽  
pp. 79-85
Author(s):  
Şenol Çelik
Keyword(s):  
Elastic Net

Bu çalışma, nüfus, tüketici fiyat indeksi (TÜFE), karayolu uzunluğu, GSYİH ve dolar kuru değişkenleri kullanılarak kişi başına otomobil sahipliğini tahmin etmek amacıyla yapılmıştır. Bu amaçla, çalışmada kullanılan veri setine çoklu regresyon analizi uygulanmıştır. Yapılan analizde çoklu bağlantı sorunu olduğu görülmüştür. Bu sorunu gidermek için alternatif regresyon yöntemlerinden Ridge, Lasso ve Elastic-Net regresyon yöntemleri uygulanmıştır. Ridge, Lasso ve Elastic-Net regresyon yöntemleri için belirleme katsayıları sırasıyla 0.953, 0.976 ve 0.952; düzeltilmiş belirleme katsayıları sırasıyla 0.932, 0.972 ve 0.943 bulunmuştur. Aynı yöntemler için hata kareler ortalamaları sırasıyla 6.044, 4.461 ve 4.655 bulunmuştur. Elde edilen bu sonuçlara göre, alternatif modellerinden en küçük hata kareler ortalaması ve en büyük belirleme katsayısına sahip Lasso regresyon yönteminin en uygun yöntem olduğu görülmüştür.

An Improved Elastic Net for Cancer Classification and Gene Selection

2010 ◽  
Vol 36 (7) ◽  
pp. 976-981 ◽  
Author(s):  
Jun-Tao LI ◽  
Ying-Min JIA
Keyword(s):  
Gene Selection ◽  
Elastic Net ◽  
Cancer Classification

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

scholarly journals Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

2021 ◽  
Author(s):  
Paulino José García-Nieto ◽  
Esperanza García-Gonzalo ◽  
José Pablo Paredes-Sánchez
Keyword(s):  
Critical Temperature ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Chemical Properties ◽  
Atomic Radius ◽  
Elastic Net ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Humpback Whales ◽  
Whale Optimization

AbstractThis study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

Self-Optimizing Support Vector Elastic Net

2020 ◽  
Vol 92 (23) ◽  
pp. 15306-15316
Author(s):  
Zewei Chen ◽  
Peter de Boves Harrington
Keyword(s):  
Elastic Net ◽  
Support Vector
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