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The scale of big data is increasing in every minute, and it becomes important to handle massive data. The familiar problem of Big data is not only huge volume but also planned in many places to provide high dimensionality in feature selection. In numerous big data application, feature selection is significant to select the essential features from the known data set and it removes unrelated and disused...
Since four decades, a sincere concern has aroused among managerial, professional, towards the satisfaction of teaching-learning objective in Academia. Huge span of time has already been spent revealing student's profile patterns using predictive modeling methods, however, very little effort is put up in identifying the causative features responsible for varied students' performances followed by decisive...
This paper presents a proposed model regarding Heterogeneous Data Reduction. The model reduces data over a heterogeneous environment through feature selection/extraction. The feature is selected/extracted directly from its data source and prepared without an initial integration for all data sources. After that the selected/extracted prepared feature is integrated into a new reduced data set Feature...
In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space...
There is little literature to introduce the approaches for the feature selection, which plays an important role in the customer churn prediction. In addition, due to the imbalanced data classification problem occurring, most of the traditional approaches ineffectively select the important features for the churn prediction. This paper proposes a new filter feature selection approach for customer churn...
Feature selection is an important problem for pattern classification systems. There are many methods for feature selection available, in which the feature selection method based on mutual information proposed by authors of Ref.[13] is one of the more effective approaches. However, it is often difficult to compute the mutual information for the continuous data whether using discretization strategy...
Iterative search margin based algorithm (Simba) has been proven effective for feature selection. However, the previously proposed model does not effectively utilize the structure information hidden in data which may have a great impact on the generalization performance of post-analysis classifiers. In this paper, we introduce a novel hypothesis-margin model incorporating structure information for...
Nowadays, data mining plays an important role in many sciences, including intrusion detection system (IDS). However, one of the essential steps of data mining is feature selection, because feature selection can help improve the efficiency of prediction rate. The previous researches, selecting features in the raw data, are difficult to implement. This paper proposes feature selection based on Euclidean...
Language identification is the process identifying predefined language in a document automatically; we focused on the Web documents in this paper. Initially, we have applied the letter frequency as features combine with neural networks in Arabic script language identification. However, reliability of selected letters of the features is a major issue to be overcome. Therefore, we propose an improved...
In pervasive computing environment, more personalized information can be achieved, and better user profile model can be built, thus personalized service can be realized. User profile is the critical aspect in personalized service of digital library. This paper proposes structure and mechanism of user profile in personalized service of digital library. The main problems during its construction are...
The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing...
Feature selection is an important processing step in machine learning. Most used feature selection methods choose top-ranking features without considering the relationships among features. In this paper, the signification of feature selection is introduced, and the goal and evaluation criteria of feature selection are analyzed. The coalitional game theory related to the feature selection is explained...
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