A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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
Machine Learning
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
IEEE Intelligent Systems
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
On Machine Learning Methods for Chinese Document Categorization
Applied Intelligence
Support Vector Machines for Text Categorization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Experiment with a hierarchical text categorization method on the WIPO-alpha patent collection
ISUMA '03 Proceedings of the 4th International Symposium on Uncertainty Modelling and Analysis
Text classification: A least square support vector machine approach
Applied Soft Computing
Advanced learning algorithms for cross-language patent retrieval and classification
Information Processing and Management: an International Journal
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Enhancing technology clustering through heuristics by using patent counts
Expert Systems with Applications: An International Journal
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
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Nowadays, decision-making activities of knowledge-intensive enterprises depend heavily on the successful classification of patents. A considerable amount of time is required to achieve successful classification because of the complexity associated with patent information and of the large number of potential patents. Several different patent classification approaches have been developed in the past, but most of these studies focus on using computational models for the International Patent Classification (IPC) system rather than using these models in real-world cases of patent classification. In contrast to previous studies that combined algorithms and the IPC system directly without using expert screening, this study proposes a novel artificial intelligence (AI)-aided patent decision-making process. In this process, an expert screening approach is integrated with a hybrid genetic-based support vector machine (HGA-SVM) model for developing a patent classification system with the high classification accuracy and generalization ability for real-world patent searching cases. The proposed approach is tested on a real-world case-an expert's patent document searching history that contains 234 patent documents of semiconductor equipment components. The research results demonstrate that our proposed hybrid genetic algorithm approach can optimize all the parameters of the SVM for developing a patent classification system with a high accuracy. The proposed HGA-SVM model is able to dynamically and automatically classify patent documents by recording and learning the experts' knowledge and logic. Finally, we propose a new decision-making process for improving the development of the SVM patent classification and searching system.