Large-scale patent classification with min-max modular support vector machines

Chu, Xiaolei; Ma, Chao; Li, Jing; Lu, Bao-Liang; Utiyama, Masao; Isahara, Hitoshi

doi:10.1109/ijcnn.2008.4634369

Cited by 9 publications

(1 citation statement)

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“…Different dictionaries and paragraph vector features are used to train Bayesian and support vector machine (SVM) classifiers respectively, with the final patent classification completed with the feature-category matrix. M3-SVM attempted to Decompose the problem of large-scale unbalanced patent classification into a set of relatively small and more balanced sub-problems, and min-max modular support vector machine is then used to handle the subproblems [17]. Later, an artificial intelligence-assisted patent decision-making method was proposed [18].…”

Section: Pac Algorithmsmentioning

confidence: 99%

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

Zhu

Fang

et al. 2020

Symmetry

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With the rapid growth of patent applications, it has become an urgent problem to automatically classify the accepted patent application documents accurately and quickly. Most previous patent automatic classification studies are based on feature engineering and traditional machine learning methods like SVM, and some even rely on the knowledge of domain experts, hence they suffer from low accuracy problem and have poor generalization ability. In this paper, we propose a patent automatic classification method via the symmetric hierarchical convolution neural network (CNN) named PAC-HCNN. We use the title and abstract of the patent as the input data, and then apply the word embedding technique to segment and vectorize the input data. Then we design a symmetric hierarchical CNN framework to classify the patents based on the word embeddings, which is much more efficient than traditional RNN models dealing with texts, meanwhile keeping the history and future information of the input sequence. We also add gated linear units (GLUs) and residual connection to help realize the deep CNN. Additionally, we equip our model with a self attention mechanism to address the long-term dependency problem. Experiments are performed on large-scale datasets for Chinese short text patent classification. Experimental results prove our proposed model’s effectiveness, and it performs better than other state-of-the-art models significantly and consistently on both fine-grained and coarse-grained classification.

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Section: Pac Algorithmsmentioning

confidence: 99%

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

Zhu

Fang

et al. 2020

Symmetry

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Bisecting data partitioning methods for Min-Max Modular Support Vector Machine

Xie

2011

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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Learning Imbalanced Data Sets with a Min-Max Modular Support Vector Machine

2007

2007 International Joint Conference on Neural Networks

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To overcome the class imbalance problem in statistical machine learning research area, re-balancing the learning task is one of the most classical and intuitive approach. Besides re-sampling, many researchers consider task decomposition as an alternative method for re-balance. Min-max modular support vector machine combines both intelligent task decomposition methods and the min-max modular network model as classifier ensemble. It overcomes several shortcomings of re-sampling, and could also achieve fast learning and parallel learning. We compare its classification performance with resampling and cost sensitive learning on several imbalanced data sets from different application areas. The experimental results indicate that our method can handle class imbalance problem efficiently.

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Large-scale patent classification with min-max modular support vector machines

Cited by 9 publications

References 10 publications

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

Bisecting data partitioning methods for Min-Max Modular Support Vector Machine

Learning Imbalanced Data Sets with a Min-Max Modular Support Vector Machine

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