cost-based feature selection
A Feature-Based Semantic Model for Automatic Product Cost Estimation
... is cost feature establishment with attribute selection that is one of the most important ...for cost estimation, most effective attributes have to be selected for this purpose ...of ...
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Low-cost scalable discretization, prediction, and feature selection for complex systems
... computational cost (B), and algorithm parallelizability (C) for scalable probabilistic approximation (SPA) (blue surfaces) and for common discretization methods: K-means clustering (16, 17) (red), NMF (19 – 24) ...
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An ensemble based feature selection methodology for case based learning
... Considering the above discussion and the rapid increase in textual data rates, it is almost impossible to extract/construct machine-readable knowledge using manual approaches. The research community prefers to use ...
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Cost-sensitive feature selection for support vector machines
... In this paper we propose a mathematical-optimization-based FS procedure embedded in one of the most popular classification procedures, namely, Support Vector Machines (SVM), accommodating asymmetric ...
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Unsupervised graph-based feature selection via subspace and pagerank centrality
... Feature selection has become an indispensable part of intelligent systems, especially with the proliferation of high di- mensional ...computational cost and significant model ...unsupervised ...
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A cluster based hybrid feature selection approach
... the feature space, SSF employs the Simplified Silhouette crite- rion (SS) [17], which justifies the name of the ...computational cost and the extent of the search performed within a given value of k for the ...
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Priorities Of Developers Based On Instance Selection and Feature Selection Technique
... Now a day’s all IT companies are influenced by software bugs. In India, Many software and IT companies waste most of the money in the wastage of Bugs. To Collect all the information related to bugs containing bug ...
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Cost-sensitive spam detection using parameters optimization and feature selection
... techniques based on machine learning techniques have been ...and feature selection have been used to reduce processing overheads while guaranteeing high detection ...account feature variable ...
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Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
... information based algorithm that analytically selects the optimal feature for ...information based feature selection algorithm can handle linearly and nonlinearly dependent data ...
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Fused Features Classification for the Effective Prediction of Chronic Kidney Disease
... Feature selection is a commonly employed dimension reduction technique in machine ...set based on certain evaluation criterion. Feature selection leads to better learning performance, ...
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A Review on Filter Based Feature Selection
... The filter is a supervised learning method and it is independent of learning algorithm which uses the characteristics of data to select and evaluate the features. It is more general, faster, requires low computational ...
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Feature Selection Based On Ant Colony
... 1995, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm), who used the 6th field (drinks), after dichotomising, as a dependent variable for ...
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Feature selection with acquisition cost for optimizing sensor system design
... automated feature selection with acquisition cost based on aggregation method, in particular employing and comparing two techniques from evolutionary computation, ...periments, based on ...
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The Impact of Cost on Feature Selection for Classifiers
... candidates for acceptance while the other, along with its subsets, is rejected. There are at least two potential ways to soften this hard threshold. Both are relatively simple but have not been tested. The first would to ...
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A survey on feature selection to perform classification using Meta Heuristic algorithms in Data Mining Domain
... sets. Feature selection can be found in many areas of data mining such as classification, clustering, association rules, and ...example, feature selection is called subset or variable ...
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Feature selection using binary particle swarm optimization with time varying inertia weight strategies
... In this paper, a wrapper FS approach using BPSO algorithm was presented. In PSO, there is only one parameter (called inertia weight w) that controls the balance between exploration and exploitation. Having a good balance ...
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Expected Divergence Based Feature Selection for Learning to Rank
... of feature selection for LTR, Geng et ...greedy feature selection method for ranking that finds the features with maximum total importance scores and minimum total similarity ...approach, ...
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A Survey on Object Detection and Tracking Algorithms
... defined. Selection of features plays an important role in the performance of the classification; hence, it is important to use a set of features that differentiate one class from the ...
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Best First and Greedy Search Based CFS- Naïve Bayes Classification Algorithms for Hepatitis Diagnosis
... In this proposed work an enhanced medical diagnostic method for addressing hepatitis diagnosis problem is developed. Experiment results on various portions of the hepatitis dataset proved that the new approach performs ...
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Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
... provide. Feature selection and classification techniques are the main tools to pursue this ...task. Feature selection techniques are meant to identify a small subset of important data within a ...
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