Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Representation and learning in information retrieval
Representation and learning in information retrieval
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Overview of the second text retrieval conference (TREC-2)
TREC-2 Proceedings of the second conference on Text retrieval conference
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster-based text categorization: a comparison of category search strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Using corpus statistics to remove redundant words in text categorization
Journal of the American Society for Information Science
Information Sciences: an International Journal
Pairwise classification and support vector machines
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A news story categorization system
ANLC '88 Proceedings of the second conference on Applied natural language processing
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using online linear classifiers to filter spam emails
Pattern Analysis & Applications
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems
Expert Systems with Applications: An International Journal
A dynamic architecture for artificial neural networks
Neurocomputing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
DSP-based hierarchical neural network modulation signal classification
IEEE Transactions on Neural Networks
Automated crime report analysis and classification for e-government and decision support
Proceedings of the 14th Annual International Conference on Digital Government Research
Expert Systems with Applications: An International Journal
The impact of preprocessing on text classification
Information Processing and Management: an International Journal
Hi-index | 12.05 |
Widespread digitization of information in today's internet age has intensified the need for effective textual document classification algorithms. Most real life classification problems, including text classification, genetic classification, medical classification, and others, are complex in nature and are characterized by high dimensionality. Current solution strategies include Naive Bayes (NB), Neural Network (NN), Linear Least Squares Fit (LLSF), k-Nearest-Neighbor (kNN), and Support Vector Machines (SVM); with SVMs showing better results in most cases. In this paper we introduce a new approach called dynamic architecture for artificial neural networks (DAN2) as an alternative for solving textual document classification problems. DAN2 is a scalable algorithm that does not require parameter settings or network architecture configuration. To show DAN2 as an effective and scalable alternative for text classification, we present comparative results for the Reuters-21578 benchmark dataset. Our results show DAN2 to perform very well against the current leading solutions (kNN and SVM) using established classification metrics.