دانلود مقاله ISI انگلیسی شماره 24146
ترجمه فارسی عنوان مقاله

رگرسیون خطی خوشه ای

عنوان انگلیسی
Clustered linear regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
24146 2002 7 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 15, Issue 3, March 2002, Pages 169–175

ترجمه کلمات کلیدی
خوشه رگرسیون خطی - الگوریتم یادگیری ماشین - روش مشتاق - الگوریتم یادگیری ماشین - روش مشتاق -
کلمات کلیدی انگلیسی
Clustering Linear regression, Machine learning algorithm, Eager approach,
پیش نمایش مقاله
پیش نمایش مقاله  رگرسیون خطی خوشه ای

Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values. Second assumption is that there are some linear approximations for this function in each subspace. Finally, there are enough training instances to determine subspaces and their linear approximations successfully. Tests indicate that if these approximations hold, CLR outperforms all other well-known machine-learning algorithms. Partitioning may continue until linear approximation fits all the instances in the training set — that generally occurs when the number of instances in the subspace is less than or equal to the number of features plus one. In other case, each new subspace will have a better fitting linear approximation. However, this will cause over fitting and gives less accurate results for the test instances. The stopping situation can be determined as no significant decrease or an increase in relative error. CLR uses a small portion of the training instances to determine the number of subspaces. The necessity of high number of training instances makes this algorithm suitable for data mining applications.