English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the rapid expansion of network information and the emergence of a large number of electronic texts, how to organize and manage this massive information has become a major challenge. Automatic text categorization technology is to study how to let the machine classify unknown text through self-learning, thus solving the difficulties encountered in manual classification. Because granular computing can reduce the knowledge in solving complex problems, it is more convenient to summarize and acquire knowledge. It has become a hotspot in recent years, and it also provides new ideas for text classification research. The rough set model of granular computing can acquire knowledge by mining decision rules. The decision process is more transparent and easy to understand. It has been paid attention to and applied in text classification research. Based on the research of existing achievements, this paper makes a further study on the application of granular computing in text categorization. After analyzing the existing feature selection methods, the feature distribution is proposed based on the relationship between feature words and categories. By calculating the distribution distance between any two feature words, the feature words with similar distribution distances are aggregated, which effectively reduces the dimension of the feature space and also avoids the individual samples caused by the existing feature selection algorithm. A phenomenon that is discarded due to features. The experimental results show that the clustering method can obtain higher classification accuracy than other feature selection methods when using SVM as the classifier. SVM performs best, and the final text classification accuracy rate can reach 85.46%. According to the correlation principle of the rough set, feature selection is made for each information granularity, the selected feature is used as the condition attribute and the coordination matrix is constructed, and the most similar sample is heuristically searched to obtain the attribute reduction set.

Text Data Processing and Classification Algorithm Based on Data Fusion and Granular Computing