Abstract
Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.
Highlights
Nowadays classification models have various limitations related to the use of a single model.Data preprocessing plays a significant role in the entire dataset
This study develops a fuzzy selection strategy that addresses fuzzy membership for selecting data to be eliminated from datasets
This study develops the support vector machine (SVM) classification model with new fuzzy selection to improve the performance of the classification problem
Summary
Nowadays classification models have various limitations related to the use of a single model.Data preprocessing plays a significant role in the entire dataset. This study attempts to handle noisy data by proposing a new algorithm for unstructured datasets. Some studies have developed outlier detection methods, for example, van der Gaag [2] used FDSTools noise profiles to obtain training datasets and a test set to analyze the impact of FDSTools noise correction for different analysis thresholds. This method was able to obtain a higher quality training dataset, leading to improved performance. Niu and Wang [3] proposed a combined model to achieve accurate prediction results. The combined model included complete empirical mode decomposition ensemble, four neural
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