[PDF] Top 20 A selective ensemble classification method on microarray data
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A selective ensemble classification method on microarray data
... of microarray data, this paper proposes a selective ensemble method teaching-learning-based optimization based to classify microarray ...with classification task, reliefF ... See full document
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Boulesteix, Anne-Laure (2005): Dimension reduction and Classification with High-Dimensional Microarray Data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... In classification interaction structures among predictors may be used explicitly or im- plicitly. In linear discriminant analysis or logistic regression a familiar way to exploit interactions is the incorporation ... See full document
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EACD: evolutionary adaptation to concept drifts in data streams
... an ensemble of clas- sifiers using Genetic Programming along with the boosting algorithm to gen- erate decision trees, each trained on different parts of the data ...programming method that generates ... See full document
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A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD
... the ensemble size prior to the combination. This phase is known as ensemble pruning, selective ensemble or ensemble thinning ...name, ensemble pruning deals with the reduction of ... See full document
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Microarray Data Analysis for Detection and Classification of Viral Infection
... But microarray technology can assess thousands of genes or proteins ...DNA microarray, if the ma- terial is RNA, it is called RNA microarray, if it is protein, the relevant microarray is ... See full document
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Ensemble Dynamics in Non-stationary Data Stream Classification
... an ensemble, especially when a concept drift happens and there are diverse classifiers in the ...voting method for selecting the output of the ensemble are unable to employ this procedure, as there ... See full document
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Detect Key Gene Information in Classification of Microarray Data
... are wavelet-based. They select Daubechies basis which has four nonzero coefficients of the compact support wavelet orthogonal basis. They use approximation coefficients and wavelet coefficients to perform mutual ... See full document
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A Comparative Study of Microarray Data Analysis for Cancer Classification
... the classification techniques are Support Vector Machine (SVM), K- Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NN) and Decision Tree (DT) ... See full document
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A comparative study of classification methods for microarray data analysis
... & Vapnik 1995) in 1995 and It has been a most influ- ential classification algorithm in recent years. SVMs are classifiers which transform the input samples into a high dimensional space by a kernel function ... See full document
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Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
... paper ensemble learning based feature selection and classifier ensemble model is pro- posed to improve classification ...from ensemble feature selection to SVM ensembles which can be achieved ... See full document
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Stable feature selection and classification algorithms for multiclass microarray data
... PLS+MCLASS method with one PLS component, but the accuracy rate for this method is sig- nificantly ...GS method with 2-class decomposition technique improves the accuracy rate and with two of the ... See full document
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Microarray Gene Expression Data Classification using a Hybrid Algorithm: MRMRAGA
... the classification model therefore, it is necessary to reduce feature in order to get good performance using feature selection ...this method, the feature(s) used to be selected which are of least interest ... See full document
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Using Hybrid and Diversity Based Adaptive Ensemble Method for Binary Classification
... Formally, a classifier is a derived function that maps instances to targets, of which all parameters are determined [9]. A machine learning algorithm is a process that estimates the parameters of the function by learning ... See full document
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Gene Selection for Tumor Classification Using Microarray Gene Expression Data
... Many methods have been proposed in the past to reduce the dimensionality of gene expression data [3]. Several machine learning techniques have been successfully applied to cancer classification using ... See full document
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A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
... financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of ... See full document
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A NOVEL HYBRID METHOD FOR GENE SELECTION IN MICROARRAY BASED CANCER CLASSIFICATION
... multidimensional data set that explain the differences in the observations and is very useful for analysis visualization and simplification of high dimensional data sets (Raychaudhuri et ...mxn data ... See full document
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Emotion Extraction Using Ensemble Classification Model In Data Mining
... an ensemble classifier schema by combining statistical machine learning classification methods and knowledge based approach for the task of recognizing emotions in various domains such as news, headlines ... See full document
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Parallel Heterogeneous Voting Ensemble for Effective Classification of Imbalanced Data
... bagged ensemble specifically designed for credit card fraud detection was proposed by Akila et ...handle data imbalance. A credit classification method to handle imbalanced data was ... See full document
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Comparative study of feature selection method of microarray data for gene classification
... Nowadays, there are a lot of selection and classification techniques that has already been studied and developed to help in better classification of microarray data. Among these techniques, ... See full document
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Study of Classification Accuracy of Microarray Data for Cancer Classification using Multivariate and Hybrid Feature Selection Method
... best classification results are reported by Li et ...discovery method and Antonov et ...cancer microarray data, which usually consists of a few hundred samples with thousands of genes as ... See full document
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