[PDF] Top 20 Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
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Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
... Ada Boost, is a machine learning algorithm underlying theory of Boosting, introduced by Freund and Scha- pire in 1995 [34]. The idea behind the Adaboost algorithm is to maintain repeatedly a set of weights over the ... See full document
16
Melanoma Detection in Dermoscopic Images using Color Features
... the accuracy of classification of malignant lesions, specificity indicates the accuracy of classification of benign lesions and accuracy indicates the overall rate of correct ... See full document
9
A comparative analysis on feature selection techniques for classification problems
... Feature selection has become the vital step in many data mining application for instances ...classification. Feature selection eliminates irrelevant attribute to obtain high ... See full document
12
Diversity in Ensemble Feature Selection
... Static Selection (SS), Weighted Voting (WV), Dynamic Selection (DS), Dynamic Voting (DV), and Dynamic Voting with Selection ...local accuracy estimates was pre-selected from the set of seven ... See full document
38
Ensemble feature subset selection technique in spam detection system
... attributes, feature subset selection and ensemble classification are necessary to address these ...the classification accuracy and computational cost are often not ...new ... See full document
6
The Analysis of GCFS Algorithm in Medical Data Processing and Mining
... Abstract: Feature selection plays a significant part in medical data processing and mining, it can reduce the dimensionalities of datasets and enhance the performance of the classifiers, and it is ... See full document
6
Worm Attack Classification Using Ensemble Classifier and DAG
... of data communication. The communication of data compromised network threats and security ...of data integrity and loss of data. For the purpose of data integrity and loss of ... See full document
9
An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL KDD Data Set
... which data mining technique is one of the most widely used for classification of ...various feature reduction method on KDD99 data ...Gradually Feature Reduced (GFR) with 19 features, ... See full document
6
Sequential Genetic Search for Ensemble Feature Selection
... In our paper, we have considered two genetic search strate- gies for ensemble feature selection. The new strategy, GAS- SEFS, consists in employing a series of genetic search proc- esses, one for ... See full document
6
Web of Service Software Reusability Prediction using Heterogenous Ensemble Classifier
... of classifier ensembles with decision-level fusion can be significant for multi- class ...an ensemble of classifiers by exploiting data from different sources and combined the base learners by means ... See full document
7
Differential Evolution Based Feature Selection and Classifier Ensemble for Named Entity Recognition
... of feature selection for NER within the frameworks of two robust machine learning algorithms, namely Conditional Random Field (CRF)(Lafferty et ...any classification technique depends on the features ... See full document
16
A SURVEY ON VARIOUS CLASSIFICATION AND NOVEL CLASS DETECTION APPROACHES FOR FEATURE EVOLVING DATA STREAM
... incremental feature ranking method in which whenever new document arrives, first check for new word, if found it is added to library and according to its frequency statistics are ...update classifier (Navie ... See full document
5
Improving Classifier Performance Using Feature Selection with Ensemble Learning
... that ensemble of neural network always perform better than the single ...automatic classifier for power system disturbance ...the classifier to a much lower dimension. Classification task was ... See full document
5
Iterative ensemble feature selection for multiclass classification of imbalanced microarray data
... the classification accuracy degrades rapidly as the num- ber of classes ...optimal feature weight- ing approach using classification-and-regression tree and SVM ...machine—recursive ... See full document
9
Search Strategies for Ensemble Feature Selection in Medical Diagnostics
... for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal ...and data mining. ... See full document
6
A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images
... classical data augmentation and CGAN to generate more CT ...a classifier is used to ensemble the class (COVID/NonCOVID) outputs of the classification ...classical feature extraction and ... See full document
17
A meta stacked ensemble probabilistic classifier
... stacked Ensemble Probabilistic Algorithm ...base ensemble classifiers (Xgboost, Linear SVM and Neural Network, sion) and further performs parameter tuning through Stacking with probabilistic distribution ... See full document
7
Hedge Classification with Syntactic Dependency Features Based on an Ensemble Classifier
... ble classifier approach to decrease FN in order to improve the recall ...semble classifier show that: along with the de- creasing of FN, FP and TP are both ...the ensemble classifier approach ... See full document
6
A Diversity-Accuracy Measure for Homogenous Ensemble Selection
... 24 data sets (For each one of them it is a question of calculating the average of time and space on 10 iterations) on a machine treating 109 instructions/ seconds (1 Gigahertz) and a memory of 3 ...credit-g ... See full document
8
A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD
... highest classification accuracy, ensemble methods have suffered significantly from their large volume of base ...the ensemble pruning ...increase classification accuracy and at ... See full document
8
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