基于粒度变换的多范畴复杂信息分类方法

Classification Method of Multi-categories Complex Information Based on Granularity Transform

针对大数据处理中多范畴复杂信息处理能力弱的问题,提出一种基于粒度变换的多范畴复杂信息对象的分类方法。首先在分类中引入映射表和误分类率阈值,然后构建等价类,设定对应不同范畴的属性,通过多种无损粒度变换和有损粒度变换的计算分析,获得多范畴复杂信息对象约泛化算子,并依此计算多范畴复杂信息分类对象的误分类率,并依误分类率对分类对象进行标引,实现有效分类。此方法在一定程度上解决多范畴复杂信息对象的分类问题,通过与另一套多范畴分类测试系统MCTS的对比,验证建立在此方法基础之上的多范畴复杂信息分类系统在误分类率上有较明显的优势。

Aiming at the weak ability to process the multi-categories of complex information in big data processing, this paper pro-poses a classification method of multi-categories complex information based on granularity transformation. First it introduces the decision attribute into the classification, gets the reduction set group of multi-categories classification attribute by calculating the value of decision class and broad-sense decision class of each category; then calculating respectively the complex comparability of each classification object based on the set group; in the end, fulfilling the automatic classification of the object based on result of complex comparability calculating. The results of contrast emulate experimentation show that system based on this kind of method gets better obviously precision ratio and recall ratio than that of traditional system.