Neighborhood Rough Set And Its Classification Algorithm

The Classical Rough Set Theory, defined on the basis of the equivalence relation, can only deal with nominal data. Numerical data must be discredited before they can be handled. As the data in the practical application usually numerical, and not so accurate itself with some errors in the procession of the measurement, which have caused inconvenience to the direct application. In this condition, Discrimination of numeric data will lead to the loss of some important information, and different treatments and strategies will also affect the final results. Therefore, the Numeric Attribute Reduction of information systems is one of the hottest issues on Rough Set Theory in recent years.This paper introduces the Neighborhood Rough Set model to the numerical Attribute Reduction and Classification Structures. Firstly, it has studied the classical Rough Set Theory and its properties, and given the Neighborhood Rough Set Theory and its related properties. Secondly, the use of Rough Sets related to neighborhood character and the nature of the neighborhood relation matrix is offered to improve the Attribute Reduction Algorithm in the paper [32], and a new and fast Attribute Reduction Algorithm based on decision table is proposed. Then by analyzing the traditional KNN Classification Method for classifying the sample which only considers the nearest point of information, without the account of the sample points of the neighbor information, the kind of neighborhood-rough-set-based classification algorithm is brought about to embrace the neighbor information. Further this article has concluded the Neighborhood Rough Set Classification Method based on Compression Mapping to compress the certain properties of large value data to a reasonable range. Such method can solve the problem of data analysis when some property value of the samples is such a great that the information of the smaller property value will be flooded in the course of the classification. Finally, the compare between the various classification methods are made, and examples are applied to prove the Classification Algorithm recommended in the paper is effective.