کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947070 | 1439562 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 265, 22 November 2017, Pages 105-115
Journal: Neurocomputing - Volume 265, 22 November 2017, Pages 105-115
نویسندگان
Yenny Villuendas-Rey, Carmen F. Rey-BengurÃa, Ángel Ferreira-Santiago, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez,