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Gene selection ensembles and classifier ensembles for medical diagnosis
2019
Biometrical Letters
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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
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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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
2020
Mathematics
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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
2013
BioMed Research International
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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]
2011
Lecture Notes in Computer Science
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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
2019
Genomics & Informatics
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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
2021
Indonesian Journal of Electrical Engineering and Computer Science
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<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
2021
IEEE Access
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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
2020
Frontiers in Genetics
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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
2021
Cognitive Computation and Systems
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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
2021
Indonesian Journal of Electrical Engineering and Computer Science
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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]
2010
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2014
IOSR Journal of Computer Engineering
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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]
2018
arXiv
pre-print
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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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