检索结果-内蒙古大学图书馆

2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Juntao Li Jingxuan Wang Yuhan Zheng Huimin Xiao College of Computer and Information Engineering Henan University of Economics and Law Beijing Obstetrics and Gynecology Hospital Capital Medical University College of Mathematics and Information Sciences Henan Normal University

ISBN: (纸本)9781509054626

The adaptive elastic net has been widely studied in the microarray classification due to the elegant performances in gene selection. However, the classification accuracy will be affected if the noise is included. As such, this paper proposes a weighted adaptive elastic net for the binary microarray classification with noise by using the distances from the sample points to both class centers. Furthermore, the performance of adaptive gene selection is proved and the solution path algorithm is developed. Finally, the results on two cancer data added 4 additional samples illustrate that the weighted adaptive elastic net can achieve considerable classification accuracy and select the genes related with diseases.

关键词： microarray classification Weighted adaptive elastic net Gene selection Noise

来源：评论

学校读者我要写书评

暂无评论

Weighted doubly regularized support vector machine and its application to microarray classification with noise

引用

NEUROCOMPUTING 2016年第Part3期173卷 595-605页

作者： Li, Juntao Wang, Yadi Cao, Yimin Xu, Cunshuan Henan Normal Univ Sch Math & Informat Sci Xinxiang 453007 Peoples R China Henan Normal Univ State Key Lab Cultivat Base Cell Differentiat Reg Xinxiang 453007 Peoples R China

A weighted doubly regularized support vector machine was proposed and its solution path algorithm was developed. By using both the distance information between classes and within each class, a double-weighted mechanism was presented, based on which the weighted doubly regularized support vector machine was proposed. A staircase function between two model parameters along the solution path direction was proposed, the multiple parameter selection problem was transformed into single parameter problem and the corresponding solution path algorithm was developed. The proposed support vector machine can adaptively identify the important genes in groups, thus encouraging an adaptive grouping effect. The experiment results on leukaemia, colon cancer, lung cancer data sets demonstrated that the proposed method can effectively select genes and reduce the influence of noise. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Support vector machine Double weights Gene selection microarray classification Solution path algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification

引用

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2016年第3期36卷 521-529页

作者： Mollaee, Maryam Moattar, Mohammad Hossein Islamic Azad Univ Mashhad Branch Young Researchers & Elite Club Mashhad Iran Islamic Azad Univ Mashhad Branch Dept Software Engn Mashhad Iran

microarray data play critical role in cancer classification. However, with respect to the samples scarcity compared to intrinsic high dimensionality, most approaches fail to classify small subset of genes. Feature selection techniques can reduce the dimension of the problem, which can reduce computational cost of the microarray data classification. However, previous studies have shown that feature extraction methods can also be useful in improving the performance of data classification. In this paper, we propose an ensemble schema for cancer diagnosis and classification that has three stages. At first, a hybrid filter based feature selection method using modified Bayesian logistic regression (BLogReg), Ttest and Fisher ratio is applied for selecting genes. In the second stage, selected genes are mapped via the proposed PSO-dICA method which is a modification of dICA. Finally, mapped features are classified using SVM classifier. To demonstrate the effectiveness of the proposed method, some traditional microarray data including Colon, Lung cancer, DLBCL, SRBCT, Leukemia-ALL and Prostate Tumor datasets are used. Experimental results show the efficiency and effectiveness of the proposed method. (C) 2016 Nalecz Institute of Biocybemetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier Sp. z o.o. All rights reserved.

关键词： Discriminant independent component analysis Feature selection microarray classification Particle swarm optimization Bayesian logistic regression

来源：评论

学校读者我要写书评

暂无评论

A novel ensemble of classifiers that use biological relevant gene sets for microarray classification

引用

APPLIED SOFT COMPUTING 2014年第0期17卷 117-126页

作者： Reboiro-Jato, Miguel Diaz, Fernando Glez-Pena, Daniel Fdez-Riverola, Florentino Univ Vigo Escuela Super Ingn Informat Dept Informat Orense Spain Univ Valladolid Escuela Univ Informat Dept Informat Segovia Spain

Since the introduction of DNA microarray technology, there has been an increasing interest on clinical application for cancer diagnosis. However, in order to effectively translate the advances in the field of microarray-based classification into the clinic area, there are still some problems related with both model performance and biological interpretability of the results. In this paper, a novel ensemble model is proposed able to integrate prior knowledge in the form of gene sets into the whole microarray classification process. Each gene set is used as an informed feature selection subset to train several base classifiers in order to estimate their accuracy. This information is later used for selecting those classifiers comprising the final ensemble model. The internal architecture of the proposed ensemble allows the replacement of both base classifiers and the heuristics employed to carry out classifier fusion, thereby achieving a high level of flexibility and making it possible to configure and adapt the model to different contexts. Experimental results using different datasets and several gene sets show that the proposal is able to outperform classical alternatives by using existing prior knowledge adapted from publicly available databases. (C) 2014 Elsevier B. V. All rights reserved.

关键词： microarray classification Biological knowledge Ensemble learning Knowledge integration Classifier fusion

来源：评论

学校读者我要写书评

暂无评论

Partly adaptive elastic net and its application to microarray classification

引用

NEURAL COMPUTING & APPLICATIONS 2013年第6期22卷 1193-1200页

作者： Li, Juntao Jia, Yingmin Zhao, Zhihua Henan Normal Univ Coll Math & Informat Sci Xingxiang 453007 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China

This paper presents a new extension of the elastic net for simultaneous gene selection and microarray classification. By introducing the proper data-driven weights to the penalty terms, the partly adaptive elastic net is proposed, which can encourage an adaptive grouping effect and reduce the influence of the wrong initial estimation. A fast-solving algorithm is also developed in the line of pathwise coordinate descent. Experiments performed on the two cancer data sets are provided to verify the obtained results.

关键词： Partly adaptive elastic net microarray classification Gene selection Pathwise coordinate descent

来源：评论

学校读者我要写书评

暂无评论

Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Seetharaman, Akila Sundersingh, Allin Christe PSG Coll Technol Dept Elect & Commun Engn Coimbatore 641004 Tamil Nadu India

DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi-objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre-convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10-fold cross-validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi-objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre-convergence with suboptimal solutions in optimization algorithms is overcome.

关键词： binary bat algorithm cancer classification gene selection microarray classification nature inspired optimization algorithm SVM

来源：评论

学校读者我要写书评

暂无评论

A Memetic Cellular Genetic Algorithm for Cancer Data microarray Feature Selection

引用

IEEE LATIN AMERICA TRANSACTIONS 2020年第11期18卷 1874-1883页

作者： Rojas, Matias Gabriel Olivera, Ana Carolina Carballido, Jessica Andrea Vidal, Pablo Javier Univ Nacl Cuyo Fac Ingn UNCuyo CONICET Inst Univ Tecnol Informac & Comunicac Mendoza Argentina Univ Nacl Sur CONICET Inst Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina

Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets. Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic' strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.

关键词： Feature Selection microarray classification Cellular Genetic Algorithm Memetic Algorithms

来源：评论

学校读者我要写书评

暂无评论

Gene Selection for microarray Cancer Data classification by a Novel Rule-Based Algorithm

引用

INFORMATION 2018年第1期9卷 6页

作者： Angulo, Adrian Pino Univ Hyogo Grad Sch Appl Informat Chuo Ku Computat Sci Ctr Bldg 5-7F Kobe Hyogo 6500047 Japan

Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster.

关键词： gene selection feature selection microarray classification

来源：评论

学校读者我要写书评

暂无评论

Histogram based Hierarchical Data Representation for microarray classification

Histogram based Hierarchical Data Representation for Microar...

引用

作者： Sandeep Kottath Polytechnic University of Catalonia

学位级别：硕士

A general framework for microarray classification classification relying on histogram based hierarchical clustering is proposed in this work. It produces precise and reliable classi- fiers based on a two-step approach. In the first step, the feature set is enhanced by histogram based features corresponding to each cluster produced via hierarchical clustering, where a parameter (maximum number of dominant genes) can be tuned based on the dataset char- acteristics. In the second step, a reliable classifier is built from a wrapper feature selection process called Improved Sequential Floating Forward Selection (IFFS) to properly choose a small feature set for the classification task. Considering the sample scarcity in the microarray datasets, a reliability parameter has been considered to improve the feature selection process along with classification error rate. Different combinations of error rate and reliability has been used as the scoring rule. Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN) classifiers have been used for this work and the performances has been compared. The potential of the proposed framework has been evaluated with three publicly available datasets: colon, lymphoma and leukaemia. The experimental results have confirmed the usefulness of the histogram based hierarchical clustering and the new representative feature generation algorithm. A gene level analysis has revealed that the best features selected by the feature selection algorithm has only very few basic constituent genes involved. The comparative re- sults showed that the proposed framework can compete with state of the art alternatives.

关键词： microarray classification histogram metagenes dominant genes hierarchical clustering EMD feature selection LDA KNN wrapper

来源：评论

学校读者我要写书评

暂无评论

Adaptive huberized support vector machine and its application to microarray classification

引用

NEURAL COMPUTING & APPLICATIONS 2011年第1期20卷 123-132页

作者： Li, Juntao Jia, Yingmin Li, Wenlin Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China

This paper proposes an adaptive huberized support vector machine for simultaneous classification and gene selection. By introducing the data-driven weights, the proposed support vector machine can adaptively identify the important genes in groups, thus encouraging an adaptive grouping effect. Furthermore, the shrinkage biases for the coefficients of important genes are largely reduced. A reasonable correlation between the two regularization parameters is also given, based on which the solution paths are shown to be piecewise linear with respect to the single regularization parameter. Experiment results on leukaemia data set are provided to illustrate the effectiveness of the proposed method.

关键词： Gene selection Adaptive grouping effect microarray classification Solution path Support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：