Gene selection algorithm by combining reliefF and mRMR
نویسندگان
چکیده
منابع مشابه
Mrmr Ba: a Hybrid Gene Selection Algorithm for Cancer Classification
The microarray technology facilitates biologist in monitoring the activity of thousands of genes (features) in one experiment. This technology generates gene expression data, which are significantly applicable for cancer classification. However, gene expression data consider as highdimensional data which consists of irrelevant, redundant, and noisy genes that are unnecessary from the classifica...
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Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomp...
متن کاملmRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informativ...
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BACKGROUND To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected...
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In recent years, multi-label learning has been used to deal with data attributed to multiple labels simultaneously and has been increasingly applied to various applications. As many other machine learning tasks, multi-label learning also suffers from the curse of dimensionality; so extracting good features using multiple labels of the datasets becomes an important step prior to classification. ...
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ژورنال
عنوان ژورنال: BMC Genomics
سال: 2008
ISSN: 1471-2164
DOI: 10.1186/1471-2164-9-s2-s27