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Document categorization in multi-agent environment with enhanced machine learning classifier

机译：借助增强的机器学习分类器在多主体环境中进行文档分类

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Text categorization task have gained the attention of researchers in last 10 years with the increase in web-based contents of documents. For searching a particular document from the web or any large document collection text or document categorization is most useful task. We demand some better system and enhanced machine learning classifiers to accomplish task of document categorization. We designed a multi-agent based system that consists of some software hybrid agents that obtains the category of a document and interact with each other to take final decision about the category and then data is fed to a machine learning classifier in order to enhance the performance. We analyzed the results of the system in form of performance measures such as accuracy, recall, precision and true negative rate. We analyzed the result in two scenarios: one is with decision of agents alone and another is with application of reinforcement clustering technique neural network. We observed that in first scenario the system's accuracy, precision and true negative rate is very good and recall measure is significantly good. After the application of reinforcement clustering technique there is no significant change in system's performance instead the recall is degraded. But still the system is producing good results in both scenarios.
机译：随着基于Web的文档内容的增加，文本分类任务已引起研究人员的关注。对于从Web或任何大型文档集合中搜索特定文档，文本或文档分类是最有用的任务。我们需要一些更好的系统和增强的机器学习分类器来完成文档分类任务。我们设计了一个基于多代理的系统，该系统由一些软件混合代理组成，这些软件混合代理获取文档的类别并相互交互以最终确定该类别，然后将数据馈送到机器学习分类器以提高性能。我们以性能指标的形式分析了系统的结果，如准确性，召回率，准确性和真实否定率。我们在两种情况下分析了结果：一种是仅由代理决定，另一种是采用增强聚类技术神经网络。我们观察到，在第一种情况下，系统的准确性，精确度和真实阴性率非常好，召回措施非常好。应用增强聚类技术后，系统性能没有显着变化，反而降低了召回率。但是，在这两种情况下，系统仍能产生良好的结果。

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