Multilayer feedforward networks are universal approximators
Neural Networks
An investigation of the use of feedforward neural networks for forecasting
An investigation of the use of feedforward neural networks for forecasting
Applied multivariate techniques
Applied multivariate techniques
Neural networks in applied statistics
Technometrics
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Computational Statistics & Data Analysis
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Journal of Management Information Systems - Special section: Data mining
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
A Novel Improvement of Neural Network Classification Using Further Division of Partition Space
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Constructing a reassigning credit scoring model
Expert Systems with Applications: An International Journal
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Applications of artificial intelligence in bioinformatics: A review
Expert Systems with Applications: An International Journal
Automated trend analysis of proteomics data using an intelligent data mining architecture
Expert Systems with Applications: An International Journal
Mammographic case base applied for supporting image diagnosis of breast lesion
Expert Systems with Applications: An International Journal
Mining protein-protein interaction information on the internet
Expert Systems with Applications: An International Journal
A GAs based approach for mining breast cancer pattern
Expert Systems with Applications: An International Journal
Rule extraction with rough-fuzzy hybridization method
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A hybrid diagnosis model for determining the types of the liver disease
Computers in Biology and Medicine
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A Software Tool for Determination of Breast Cancer Treatment Methods Using Data Mining Approach
Journal of Medical Systems
Disease diagnosis using query-based neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
International Journal of Information and Communication Technology
Artificial neural networks applied to cancer detection in a breast screening programme
Mathematical and Computer Modelling: An International Journal
Change point determination for a multivariate process using a two-stage hybrid scheme
Applied Soft Computing
Hybrid intelligent modeling schemes for heart disease classification
Applied Soft Computing
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Data mining is a very popular technique and has been widely applied in different areas these days. The artificial neural network has become a very popular alternative in prediction and classification tasks due to its associated memory characteristics and generalization capability. However, the relative importance of potential input variables and the long training process have often been criticized and hence limited its application in handling classification problems. The objective of the proposed study is to explore the performance of data classification by integrating artificial neural networks with the multivariate adaptive regression splines (MARS) approach. The rationale under the analyses is firstly to use MARS in modeling the classification problem, then the obtained significant variables are used as the input variables of the designed neural networks model. To demonstrate the inclusion of the obtained important variables from MARS would improve the classification accuracy of the networks, diagnostic tasks are performed on one fine needle aspiration cytology breast cancer data set. As the results reveal, the proposed integrated approach outperforms the results using discriminant analysis, artificial neural networks and multivariate adaptive regression splines and hence provides an efficient alternative in handling breast cancer diagnostic problems.