The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness---the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elim...

Attribute selection is a data preprocessing step which aims at identifying relevant attributes for a target data mining task – specifically in this article, the classification task. Previously, we have proposed a new attribute selection strategy – based on a lazy learning approach – which postpones the identification of relevant attributes until an instance is submitted for classification. Expe...

A fast response time is a major objective for Mobile Geographic Information System (GIS) applications. This study provides a solution for improving the performance of response time by a dynamic data model. A conceptual dynamic data model is proposed, which covers (a) ‘‘position’’ information of selected geographic objects relevant to the GIS user’s interest within his current location, (b) the ...

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with th...

This paper presents a novel energy constrained target K-coverage algorithm, and the proposed algorithm is suitable to be exploited in heterogeneous wireless sensor networks. Particularly, the network activity in heterogeneous wireless sensor networks in this paper is organized in rounds, and each round is constructed by initial step and information sensing step. Furthermore, to prolong the netw...

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant irrelevant information, the selection, evaluation, combination of those large-scale pose huge challenges to traditional methods. Fuzzy rough have emerged as a powerful vehicle deal with uncertain fuzzy in problems that involve very large number variables be analyzed short time. In order f...

A number of approaches have been proposed to determine the weights for multiple attribute decision making. However, resultant are usually assumed be fixed, making it lack tolerance accommodate variation if patterns subsequent data subject change. This article proposes a method facilitate adjustment weights, which accommodates relevant characteristics. model is first constructed that able expres...

This paper studies the eeects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, M-of-N, and X-of-N representations). To reduce eeects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With diierent option se...

Most of data in a multi attribute decision making (MADM) problem are unstable and changeable, and thus sensitivity analysis can effectively contribute to making proper decisions. Here, we offer a new method for sensitivity analysis of multi-attribute decision making problems so that by changing one element of decision making matrix, we can determine changes in the results of a decision making p...

The process of reducing the number of attributes from an information system is known as attribute reduction problem. The action of attribute reduction problem is an important step of pre-processing in data mining. In the attribute reduction process, reduction is performed with considerations for minimum information loss. Having a dataset with discrete attribute values, it is possible to find a ...