Using Gain Ratio for Attribute Selection
Fuzzy-rough Information Gain Ratio Approach to Filter-wrapper Feature Selection
... feature selection, although using a dependency degree measure might be useful to select a subset of features that preserves the meaning of the features and is rarely dependent on the other features, it is ...
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Selection of Temporary Landfill using Fuzzy Multiple Attribute
... Therefore it is needed the criteria that can be used to determine the location of landfill that is feasible and meets the requirements. The requirements are stated in the Indonesian National Standard (SNI) 03-3241-1994 ...
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Ratio-Type Estimators in Stratified Random Sampling using Auxiliary Attribute
... Some ratio-type estimators have been proposed in stratified random sampling using auxiliary ...combined ratio estimator and it is shown that the proposed estimators are more efficient than combined ...
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Ratio Estimators in Simple Random Sampling Using Information on Auxiliary Attribute
... Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are ...
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Association Rule Generation using Attribute Information Gain and Correlation Analysis for Classification
... Prof & Head, Department of CSE, GITAM University, Hyderabad Abstract The discovery of association rules is an important data- mining task for which many algorithms have been proposed. However, the efficiency of these ...
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Classification Algorithms with Attribute Selection: an evaluation study using WEKA
... -------------------------------------------------------------------ABSTRACT--------------------------------------------------------------- Attribute or feature selection plays an important role in the ...
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Improving Lazy Attribute Selection
... traditionally, attribute selection techniques are executed as a data preprocessing step, making their results definitive from that point ...filter attribute selection strategy – based on a ...
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Performance Evaluation of Gene based Ontology Using Attribute Selection Methods
... these selection methods are used to gain the two goals: reduce the repetitious between GO terms and select the instances which are having higher relevance for class ...
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Exploring Attribute Selection in Hierarchical Classification
... implemented using the JAVA programming language, incor- porating algorithms and functions of the data mining tool WEKA ...Filter attribute selection method, provided in the WEKA tool with the name ...
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An Approach to Detect Credit Card Frauds using Attribute Selection and Ensemble Techniques
... 3.1 Naïve Bayes Naïve Bayes classifier is an uncomplicated and prevailing algorithm for the classification task. Even if we are running on a data set with millions of accounts with some attributes, it is recommended to ...
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Performance Improvement of Classifier Using Attribute Selection with Association Rule Mining Technique
... Fig 3: Comparative Analysis for the Dataset Mushroom VI. CONCLUSION This paper proposes the method of selecting best attribute from the best rules optimized using apriori algorithm. It is observed that the ...
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DataMining Clustering Techniques in the Prediction of Heart Disease using Attribute Selection Method
... Options:-direction -- Set the direction of the search. lookupCacheSize --Set the maximum size of the lookup cache of evaluated subsets.This is expressed as a multiplier of the number of attributes inthe data set. ...
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Multi-Variate Attribute Selection for Agricultural Data
... CHAPTER 4. DATA PREPARATION Before proceeding to the results, the data set used in this study and its preparation will be described. Because of the uniqueness of agriculture, the data set used in this study draws upon ...
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Classification and Attribute Selection Analysis for Hexapoda miRNA
... Information gain, SVM classifier for dominating attribute selection for four hexapoda ...essential attribute for pre-miRNA prediction are similar and conversed region within a particular ...
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Benchmarking attribute selection techniques for microarray data
... Feature selection helps to improve prediction quality, reduce the computation time, complexity of the model and build models that are easily ...Feature selection removes the irrelevant and redundant ...
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Benchmarking Attribute Selection Techniques for Data Mining
... 2 Attribute Selection Techniques Attribute selection techniques can be categorized according to a number of cri- ...by using accuracy estimates provided by the actual target learning ...
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Attribute Selection Methods in Rough Set Theory
... are 2 N subsets of attributes, and the exhaustive method is an NP-hard problem [5]. In practice, heuristic algorithms have to be considered. 1.4 Reduct algorithms Johnson’s algorithm is a simple greedy reduct algorithm. ...
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Feature selection using information gain for improved structural-based alert correlation
... feature selection method is proposed to obtain the significant ...mation gain entropy in decreasing ...accuracy using 2000 DARPA intrusion detection scenario-specific ...
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SCHEDULING AUTOMOBILE SELECTION BY USING MULTI ATTRIBUTE UTILITY TECHNIQUE & LINEAR PROGRAMMING
... pick and survey creative substitutions. Decision producers' sentiments are evaluated towards the positioning of substitutions. (Sadaoui and Shil, 2016)multi-dimensional things is a significant preliminary, that requires ...
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Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems
... We compare the layered approach with the work in [19] where it is the most closely related work to our work. The authors in [19] addressed these two issues of Accuracy and Efficiency using Conditional Random ...
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