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Gene selection ensembles and classifier ensembles for medical diagnosis

M. Ćwiklińska-Jurkowska
2019 Biometrical Letters  
Additionally, the procedure of heterogeneous combining of five base classifiers—k-nearest neighbors, SVM linear and SVM radial with parameter c=1, shrunken centroids regularized classifier (SCRDA) and  ...  Based on the misclassification rates for the three examined microarray data sets, for any examined ensemble of classifiers, the combining of gene selection methods was not superior to single PAM or SAM  ...  Hence, for the Colon data set the plots indicate significant differences between the heterogeneous ensemble HeterMerge2 and bagging trees for 50-80 genes (Fig. 2) .  ... 
doi:10.2478/bile-2019-0007 fatcat:2arsxfrz4zbmxmzolhiouutuqy

Neighbor Embedding Feature Selected Light Gradient Boosting Classification for Breast Cancer Detection with Gene Expression Data

The boosting algorithm initially constructs' number of weak learners i.e. bivariate regression tree to classify the input expression data into normal or cancerous with the selected features.  ...  Next, the classification of the gene expression data is done with the help of steepest descent light gradient boosting algorithm.  ...  The ensemble classifier initializes the empty set of weak learners as a bivariate regression tree with the number of training gene expression data.  ... 
doi:10.35940/ijitee.k1108.09811s19 fatcat:nm3nadnxkreujpvl5ndtfw4ut4

Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data

Abhijeet R Patil and Sangjin Kim
2020 Mathematics  
Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correlated data.  ...  dealing data with the high correlation structures.  ...  The tree-based ensemble methods RF and AB with RLFS also attained good accuracies but were not the best compared to the ERRM classifier.  ... 
doi:10.3390/math8010110 fatcat:ynjz3e3c4jhtfhx4tm76sbufjq

An Efficient Ensemble Learning Method for Gene Microarray Classification

Alireza Osareh, Bita Shadgar
2013 BioMed Research International  
Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification.  ...  On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology.  ...  Rotation Forest is an ensemble classification approach which is built with a set of decision trees.  ... 
doi:10.1155/2013/478410 pmid:24024194 pmcid:PMC3759279 fatcat:uhdxokqygralbmvediqqz55owy

Ensemble Logistic Regression for Feature Selection [chapter]

Roman Zakharov, Pierre Dupont
2011 Lecture Notes in Computer Science  
It specifically addresses high dimensional data with few observations, which are commonly found in the biomedical domain such as microarray data.  ...  It also outperforms a selection based on Random Forests, another popular embedded feature selection from an ensemble of classifiers.  ...  In this paper, we propose a novel approach to perform feature (e.g. gene) selection jointly with the estimation of a binary classifier.  ... 
doi:10.1007/978-3-642-24855-9_12 fatcat:rtg7cgmocfhm5abwhyfuju6npe

Review of statistical methods for survival analysis using genomic data

Seungyeoun Lee, Heeju Lim
2019 Genomics & Informatics  
model with high-dimensional genomic data.  ...  However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival  ...  Ensemble methods Ensemble methods are based on the wisdom of "the crowd," i.e., a new classifier produced by aggregating or voting from a group of classifiers.  ... 
doi:10.5808/gi.2019.17.4.e41 pmid:31896241 pmcid:PMC6944043 fatcat:dw7rubh7v5a3hcgptsyqnydk6a

Knowledge discovery from gene expression dataset using bagging lasso decision tree

Umu Sa'adah, Masithoh Yessi Rochayani, Ani Budi Astuti
2021 Indonesian Journal of Electrical Engineering and Computer Science  
<p>Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features.  ...  The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying.  ...  ., the head of Laboratorium Sentral Ilmu Hayati (LSIH) Universitas Brawijaya, for giving us knowledge about genes.  ... 
doi:10.11591/ijeecs.v21.i2.pp1151-1159 fatcat:vhrkxty4cbccjc2hxbgykrbojy

An effective tumor classification with deep forest and self-training

Zhanbo Chen, Zhanbo Chen, Xiaojun Sun, Lili Shen
2021 IEEE Access  
In recent years, tumor classification based on the gene expression omnibus has become a continuous attention field in the area of bioinformatics .  ...  We wish training style that samples can be implemented to train by from high-to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used  ...  Deep forest is an incremental classifier based on multiple decision trees ensemble approach, which utilizes non-differentiable modules to construct deep models.  ... 
doi:10.1109/access.2021.3096241 fatcat:imot75ahnjglxmef6mbua37s6i

Identification of Orphan Genes in Unbalanced Datasets Based on Ensemble Learning

Qijuan Gao, Xiu Jin, Enhua Xia, Xiangwei Wu, Lichuan Gu, Hanwei Yan, Yingchun Xia, Shaowen Li
2020 Frontiers in Genetics  
To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using  ...  The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately  ...  The results showed that the ensemble classifiers method classified the orphan and non-orphan genes more precisely than the single classifiers, and among the five ensemble models with XGBoost, the SMOTE-ENN-XGBoost  ... 
doi:10.3389/fgene.2020.00820 pmid:33133122 pmcid:PMC7567012 fatcat:jke5a6vm4ba6xoqozhtk3qxp6e

Ensemble learning‐based classification of microarray cancer data on tree‐based features

Guesh Dagnew, B.H. Shekar
2021 Cognitive Computation and Systems  
A random forest (RF) tree-based feature selection and ensemble learning based on hard voting and soft voting is proposed to classify microarray cancer data using six different base classifiers.  ...  The selected features due to RF tree are submitted to the base classifiers as the training set.  ...  The authors proposed an ensemble learning method to classify microarray cancer data using RF tree-based feature selection.  ... 
doi:10.1049/ccs2.12003 fatcat:zue67bbainbrrcjgngqbum3eai

Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

Micheal Olaolu Arowolo, Marion O. Adebiyi, Ayodele A. Adebiyi, Olatunji J. Okesola
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures.  ...  The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.</p>  ...  Tree model enhancement for classifying certain ensembled features was proposed using an ensemble-based feature selection, random trees and wrapper-based feature selection system in developing a classification  ... 
doi:10.11591/ijeecs.v21.i2.pp1073-1081 fatcat:x7zwdydc6jecffhw3td3xb64bm

Automated DNA Motif Discovery [article]

W. B. Langdon, Olivia Sanchez Graillet, A. P. Harrison
2010 arXiv   pre-print
Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs.  ...  The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal.  ...  Table 1 : 1 Number and type of each non-protein coding Ensembl human gene data have exactly 60 bases taken from the start of the Ensembl transcript.  ... 
arXiv:1002.0065v1 fatcat:scdo4jlcxjafxdeowvp64n5zjm

A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method with Ensemble Classifier for Microarray Data [article]

Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian
2021 arXiv   pre-print
Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data.  ...  However, microarray data are often associated with challenges such as small sample size, a significant number of genes, imbalanced data, etc. that make classification models inefficient.  ...  based on chaos theory and non-dominated sorting  Using multi-filter to pre-process data and reduce the number of data genes  Selecting effective genes simultaneously with optimizing KELM classifier  ... 
arXiv:2101.00819v1 fatcat:pxcmxrsiizgypi4nud6gp24uzm

A Survey on: Stratified mapping of Microarray Gene Expression datasets to decision tree algorithm aided through Evolutionary Design

Ms. Neha V.Bhatambarekar, Prof. Payal S. Kulkarni
2014 IOSR Journal of Computer Engineering  
Analyzing gene expression data is a challenging task since the large number of features against the shortage of available examples can be prone to over fitting.  ...  Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans.  ...  Fig : Major building blocks of Decision Tree Split Genes These genes are concerned with the task of selecting the attribute to split the data in the current node of the decision tree.  ... 
doi:10.9790/0661-16650106 fatcat:4iatqoipf5edblrtwmr6mclxoa

A Nonparametric Ensemble Binary Classifier and its Statistical Properties [article]

Tanujit Chakraborty and Ashis Kumar Chakraborty and C.A. Murthy
2018 arXiv   pre-print
In this work, we propose an ensemble of classification trees (CT) and artificial neural networks (ANN).  ...  Our proposed nonparametric ensemble classifier doesn't suffer from the 'curse of dimensionality' and can be used in a wide variety of feature selection cum classification problems.  ...  But the ensemble classifier will have an edge where the data analysis requires important variable selections in the early stage followed by predictions using classifiers for limited data sets.  ... 
arXiv:1804.10928v2 fatcat:ftfla4e6avbz5neheblvmwtroa
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