Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Introduction to the theory of neural computation
Introduction to the theory of neural computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Readings in database systems (2nd ed.)
Readings in database systems (2nd ed.)
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Theory and application of cellular automata for pattern classification
Fundamenta Informaticae - Special issue on cellular automata
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
A self-organizing neural tree for large-set pattern classification
IEEE Transactions on Neural Networks
ANN-DT: an algorithm for extraction of decision trees from artificial neural networks
IEEE Transactions on Neural Networks
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
Classification trees with neural network feature extraction
IEEE Transactions on Neural Networks
A balanced neural tree for pattern classification
Neural Networks
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
Applied Soft Computing
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This paper presents the design of a hybrid learning model, termed as neural network tree (NNTree). It incorporates the advantages of both decision tree and neural network. An NNTree is a decision tree, where each non-terminal node contains a neural network. The idea of the proposed method is to use the framework of multilayer perceptron to design tree-structured pattern classifier. At each non-terminal node, the multilayer perceptron partitions the dataset into m subsets, m being the number of classes in the dataset present at that node. The NNTree is designed by splitting the non-terminal nodes of the tree by maximizing classification accuracy of the multilayer perceptron. In effect, it produces a reduced height m-ary tree. The versatility of the proposed scheme is illustrated through its application in diverse fields. The effectiveness of the hybrid algorithm, along with a comparison with other related algorithms, has been demonstrated on a set of benchmark datasets. Simulation results show that the NNTree achieves excellent performance in terms of classification accuracy, size of the tree, and classification time.