کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
407056 678125 2013 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Locally linear reconstruction based missing value imputation for supervised learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Locally linear reconstruction based missing value imputation for supervised learning
چکیده انگلیسی

Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with missing values. First, we investigate how missing values degrade the prediction performance with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset.


► A new missing value imputation method based on LLR is proposed.
► The comparative study is performed to verify the effectiveness of LLR imputation.
► LLR imputation method outperforms other methods in classification and regression.
► The performance of LLR imputation is as good as that of the complete data sets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 118, 22 October 2013, Pages 65–78
نویسندگان
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