FEATURE SELECTION AND CLASSIFICATION OF INTRUSION DETECTION SYSTEM USING ROUGH SET

With the expansion of computer network there is a challenge to compete with the intruders who can easily break into the system. So it becomes a necessity to device systems or algorithms that can not only detect intrusion but can also improve the detection rate. In this paper we propose an intrusion detection system that uses rough set theory for feature selection, which is extraction of relevant attributes from the entire set of attributes describing a data packet and used the same theory to classify the packet if it is normal or an attack. After the simplification of the discernibility matrix we were to select or reduce the features. We have used Rosetta tool to obtain the reducts and classification rules. NSL KDD dataset is used as training set and is provided to Rosetta to obtain the classification rules

Credit Scoring Based on Hybrid Data Mining Classification

The credit scoring has been regarded as a critical topic. This study proposed four approaches combining with the NN (Neural Network) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different approaches combined with NN classifier were constructed by selecting features. NN classifier combines with conventional statistical LDA, Decision tree, Rough set and F-score approaches as features preprocessing step to optimize feature space by removing both irrelevant and redundant features. The procedure of the proposed algorithm is described first and then evaluated by their performances. The results are compared in combination with NN classifier and nonparametric Wilcoxon signed rank test will be held to show if there has any significant difference between these approaches. Our results suggest that hybrid credit scoring models are robust and effective in finding optimal subsets and the compound procedure is a promising method to the fields of data mining

The Use of Rough Set Theory in Determining the Preferences of the Customers of an Insurance Agency

In today’s market environment a fierce competition is being experienced. It can be clearly stated that the businesses that determine the customer profiles well and manufacture related products in accordance with the requests/needs of the customers gain superiority over their rivals. Within this scope, this fact is also an important issue for the companies that are trying to keep up with other competitors in the insurance sector. In this study, this critical problem of EPD which is an agency of Allianz Insurance was solved by using Rough Set Theory (RST) method. Ten condition attributes (i.e. age, gender, etc.) were examined in the study. Decision attribute is the variable of the insurance type which includes individual retirement, health and life insurances. With the method of RST, a set of rules were identified which may help in developing strategies that will bring in new customers to EPD while keeping present ones. The attained results were presented to the executives of EPD. The executives have re-determined their marketing strategies in compliance with these results and exercised these strategies accordingly. Feedbacks from the executives indicated that the RST helps in facilitating the development of marketing strategies based on the characteristics of the customers and determining their profiles. Keywords: Rough set theory, customer’s profile, insurance, decision rule

Value reducts and bireducts: A comparative study

In Rough Set Theory, the notion of bireduct allows to simultaneously reduce the sets of objects and attributes contained in a dataset. In addition, value reducts are used to remove some unnecessary values of certain attributes for a specific object. Therefore, the combination of both notions provides a higher reduction of unnecessary data. This paper is focused on the study of bireducts and value reducts of information and decision tables. We present theoretical results capturing different aspects about the relationship between bireducts and reducts, offering new insights at a conceptual level. We also analyze the relationship between bireducts and value reducts. The studied connections among these notions provide important profits for the efficient information analysis, as well as for the detection of unnecessary or redundant information