Search

In real world, there are a lot of knowledge such as the following: most human beings that are infected by a kind of virus suffer from a corresponding disease, but a small number human beings do not. Which are the factors that negate the effects of the virus? Standard rough set method can induce simplified rules for classification, but cannot generate this kind of knowledge directly. In this paper, we propose two algorithms to find the factors. In the first algorithm, the typical rough set method is used to generate all the variable precision rules firstly; secondly reduce attributes and generate all the non-variable precision rules; lastly compare the variable precision rules and non-variable precision rules to generate the factors that negate the variable precision rules. In the second algorithm, firstly, induce all the variable precision rules; secondly, select the examples corresponding to the variable precision rules to build decernibility matrixes; thirdly, generate the factors that negate the variable precision rules. Three experimental results show that using the two algorithms can get the same results and the computational complexity of the second algorithm is largely less than the firs one. [ABSTRACT FROM AUTHOR]

Comparative knowledge is the main and important knowledge in our knowledge-box with which we differentiate things or get the difference between things. Using rough set we can induce not only the classification rules but also the comparative knowledge. For example, in comparison with men who do not smoke, the women who too do not smoke are more susceptible to suffer from lung cancer. From our SARS data set, using rough set theory we have induced the comparative knowledge such as: when the attribute hemoglobin's values of the patients are the same, and if the patients' states of illness are 3 (3 means that the state of illness is critical), then the attribute lymph's values are mostly smaller than the ones of the patients whose states of illness are 2(2 means that the state of illness is serious) and so on. [ABSTRACT FROM AUTHOR]

In KDD procedure, to fill in missing data typically requires a very large investment of time and energy - often 80% to 90% of a data analysis project is spent in making the data reliable enough so that the results can be trustful. In this paper, we propose a SVM regression based algorithm for filling in missing data, i.e. set the decision attribute (output attribute) as the condition attribute (input attribute) and the condition attribute as the decision attribute, then use SVM regression to predict the condition attribute values. SARS data set experimental results show that SVM regression method has the highest precision. The method with which the value of the example that has the minimum distance to the example with missing value will be taken to fill in the missing values takes the second place, and the mean and median methods have lower precision. [ABSTRACT FROM AUTHOR]

For SVM classifier, Pre-selecting data is necessary to achieve satisfactory classification rate and reduction of complexity. According to Rough Set Theory, the examples in boundary region of a set belong to two or more classes, lying in the boundary of the classes, and according to SVM, support vectors lie in the boundary too. So we use Rough Set Theory to select the examples of boundary region of a set as the SVM classifier set, the complexity of SVM classifier would be reduced and the accuracy maintained. Experiment results of SARS data indicate that our schema is available in both the training and prediction stages. [ABSTRACT FROM AUTHOR]