This paper addresses the problem of Near Duplicate document. Propose a new method to detect near duplicate document from a large collection of document set. This method is classified into three steps. Feature selection, similarity measures and discriminant function. Feature selection performs pre-processing; calculate the weight of each terms and heavily weighted term is selected as a features ...

This paper proposes an efficient, Chi-Square-based, feature selection method for Arabic text classification. In Data Mining, feature selection is a preprocessing step that can improve the classification performance. Although few works have studied the effect of feature selection methods on Arabic text classification, limited number of methods was compared. Furthermore, different datasets were u...

Support Vector Machines (SVM) is a powerful classification technique in data mining and has been successfully applied to many real-world applications. Parameter selection of SVM will affect classification performance much during training process. However, parameter selection of SVM is usually identified by experience or grid search (GS). In this study, we use Taguchi method to make optimal appr...

This paper describes data mining in finance by discussing financial tasks and specifics of methodologies and techniques in this data mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based, and relational methodologies. 81 1 This paper is a modified version of authors...

Feature selection is an important issue in the research areas of machine learning and data mining. It reduces the dimensionality of data and enhances the performance of data analysis and interpretability, such as clustering or classification algorithms. This paper proposes a feature selection method based on support vector machines and distance-based cumulative distribution functions. This meth...

We present a method for dimension reduction applied to visual data mining in order to reduce the user cognitive load due to the density of data to be visualized and mined. We use consensus theory to address this problem: the decision of a committee of experts (in our case existing attribute selection methods) is generally better than the decision of a single expert. We illustrate the choices op...

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

Data mining and machine learning techniques have been used in several scientific applications including software fault predictions in large space systems. State-of the-art research revealed that existing space systems succumb to enigmatic software faults leading to critical loss of life and capital. This article presents a novel approach to solve this issue of overlooking software faults by uti...

We describe the method used in our final submission to KDD Cup 2009 as well as a selection of promising directions that are generally believed to work well but did not justify our expectations. Our final method consists of a combination of a LogitBoost and an ADTree classifier with a feature selection method that, as shaped by the experiments we have conducted, have turned out to be very differ...

Data classification is a challenging task in era of big data due to high number of features. Feature selection is a step in process of knowledge discovery in data that aims to reduce dimensionality and improve the classification performance. The purpose of this research is to define new techniques for feature selection in order to improve classification accuracy and reduce the time required for...