کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530346 869760 2014 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers
ترجمه فارسی عنوان
محاسبه آماری طرح های وزن گذاری ویژگی ها از طریق تخمین داده ها برای نزدیکترین طبقه بندی های همسایه
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• Redundant features may deteriorate the performance of the Nearest Neighbor rule.
• This paper proposes a new feature weighting classifier to overcome this problem.
• Imputation methods are used to estimate a distribution of values for each feature.
• A statistical test sets weights based on differences of the feature distributions.
• Our proposal outperforms the rest of the classifiers considered in the comparisons.

The Nearest Neighbor rule is one of the most successful classifiers in machine learning. However, it is very sensitive to noisy, redundant and irrelevant features, which may cause its performance to deteriorate. Feature weighting methods try to overcome this problem by incorporating weights into the similarity function to increase or reduce the importance of each feature, according to how they behave in the classification task. This paper proposes a new feature weighting classifier, in which the computation of the weights is based on a novel idea combining imputation methods – used to estimate a new distribution of values for each feature based on the rest of the data – and the Kolmogorov–Smirnov nonparametric statistical test to measure the changes between the original and imputed distribution of values. This proposal is compared with classic and recent feature weighting methods. The experimental results show that our feature weighting scheme is very resilient to the choice of imputation method and is an effective way of improving the performance of the Nearest Neighbor classifier, outperforming the rest of the classifiers considered in the comparisons.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 47, Issue 12, December 2014, Pages 3941–3948
نویسندگان
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