Abstract
Extreme Learning Machine, ELM, is a recently available learning algorithm for single layer feedforward neural network. Compared with classical learning algorithms in neural network, e.g. Back Propagation, ELM can achieve better performance with much shorter learning time. In the existing literature, its better performance and comparison with Support Vector Machine, SVM, over regression and general classification problems catch the attention of many researchers. In this paper, the comparison between ELM and SVM over a particular area of classification, i.e. text classification, is conducted. The results of benchmarking experiments with SVM show that for many categories SVM still outperforms ELM. It also suggests that other than accuracy, the indicator combining precision and recall, i.e. F 1 value, is a better performance indicator.
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Keywords
- Support Vector Machine
- Feature Selection
- Extreme Learning Machine
- Category Support Vector Machine
- Extreme Learning Machine Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines: and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Dumais, S., Chen, H.: Hierarchical classification of Web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 2000 (2000)
Flach, P.A.: On the state of the art in machine learning: a personal review. Artificial Intelligence 13, 199–222 (2001)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. In: International Joint Conference on Neural Networks, IJCNN 2004 (2004)
Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: Machine Learning: ECML 1998, Tenth European Conference on Machine Learning (1998)
Joachims, T.: Transductive Inference for Text Classification using Support Vector Machines. In: Proceedings of the 16th International Conference on Machine Learning, ICML (1999)
Kasabov, N.: Data mining and knowledge discovery using neural networks (2002)
Leopold, E., Kindermann, J.: Text Categorization with Support Vector Machines - How to Represent Texts in Input Space. Machine Learning 46, 423–444 (2002)
Liu, Y., Loh, H.T., Tor, S.B.: Building a Document Corpus for Manufacturing Knowledge Retrieval. In: Singapore MIT Alliance Symposium 2004 (2004)
Mangasarian, O.L.: Data Mining via Support Vector Machines. In: 20th International Federation for Information Processing (IFIP) TC7 Conference on System Modeling and Optimization (2001)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Mitchell, T.M.: Machine Learning. McGraw-Hill Companies, Inc, New York (1997)
Ng, H.T., Goh, W.B., Low, K.L.: Feature selection, perception learning, and a usability case study for text categorization. In: ACM SIGIR Forum, Proceedings of the 20th annual in-ternational ACM SIGIR conference on Research and development in information retrieval (1997)
Ruiz, M.E., Srinivasan, P.: Hierarchical Neural Networks for Text Categorization. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999)
Ruiz, M.E., Srinivasan, P.: Hierarchical Text Categorization Using Neural Networks. Information Retrieval 5, 87–118 (2002)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 1st edn. MIT Press, Cambridge (2001)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys (CSUR) 34, 1–47 (2002)
Sun, A., Lim, E.-P.: Hierarchical Text Classification and Evaluation. In: Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001 (2001)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)
Wiener, E.D., Pedersen, J.O., Weigend, A.S.: A neural network approach to topic spotting. In: Proceedings of SDAIR-95, 4th Annual Symposium on Document Analysis and Information Retrieval (1995)
Weigend, A.S., Wiener, E.D., Pedersen, J.O.: Exploiting hierarchy in text categorization. Information Retrieval 1, 193–216 (1999)
Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 1, 69–90 (1999)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999)
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Liu, Y., Loh, H.T., Tor, S.B. (2005). Comparison of Extreme Learning Machine with Support Vector Machine for Text Classification. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_55
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DOI: https://doi.org/10.1007/11504894_55
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