TY - JOUR
T1 - The Naïve Associative Classifier (NAC)
T2 - A novel, simple, transparent, and accurate classification model evaluated on financial data
AU - Villuendas-Rey, Yenny
AU - Rey-Benguría, Carmen F.
AU - Ferreira-Santiago, Ángel
AU - Camacho-Nieto, Oscar
AU - Yáñez-Márquez, Cornelio
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/11/22
Y1 - 2017/11/22
N2 - 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.
AB - 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.
KW - Bank Telemarketing
KW - Bank deposits
KW - Mixed data
KW - Savings
KW - Similarity
KW - Supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85020482428&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2017.03.085
DO - 10.1016/j.neucom.2017.03.085
M3 - Artículo
SN - 0925-2312
VL - 265
SP - 105
EP - 115
JO - Neurocomputing
JF - Neurocomputing
ER -