Titlebook: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing; 10th International C Dominik Ślęzak,JingTao Yao,Xiaohua Hu Conference proceedi

Ophthalmologist

Prediction Mining – An Approach to Mining Association Rules for Predictione to predict the consequent of the rule. But not all of association rules may be suitable for prediction. In this paper, we investigate the properties of rules for prediction, and develop an approach called . — mining a set of association rules that are useful for prediction. Prediction mining disco

赤字

A Rough Set Based Model to Rank the Importance of Association Rulesre more useful, interesting and important. We introduce a rough set based process by which a rule importance measure is calculated for association rules to select the most appropriate rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to g

哀求

paltry

Rough Learning Vector Quantization Case Generation for CBR Classifiersantization (LVQ). If the feature values of the cases are numerical, fuzzy sets are firstly used to discretize the feature spaces. Secondly, a fast rough set-based feature selection method is built to identify the significant features. The representative cases (prototypes) are then generated through

LAVE

ML-CIDIM: Multiple Layers of Multiple Classifier Systems Based on CIDIM this paper we present a method to improve even more the accuracy: ML-CIDIM. This method has been developed by using a multiple classifier system which basic classifier is CIDIM, an algorithm that induces small and accurate decision trees. CIDIM makes a random division of the training set into two s

Creatinine-Test

改进

Tonometry

Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers (one versus one). The 1-v-r approach tends to have higher training time, while 1-v-1 approaches tend to create a large number of binary classifiers that need to be analyzed and stored during the operational phase. This paper describes how rough set theory may help in reducing the storage requiremen

Mundane

Increment

Towards Human-Level Web Intelligenceand advanced Information Technology (IT) on the next generation of Web-empowered systems, services, and environments. The WI technologies revolutionize the way in which information is gathered, stored, processed, presented, shared, and used by virtualization, globalization, standardization, personalization, and portals.