An Integrative Approach to Identifying Biologically Relevant Genes

Zhao, Zheng; Wang, Jiangxin; Sharma, Shashvata; Agarwal, Nitin; Liu, Huan; Chang, Yung

doi:10.1137/1.9781611972801.58

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…Figure 1.8 demonstrates how multi-source feature selection works. Recent study shows that the capability of using multiple data and knowledge sources in feature selection may effectively enrich our information and enhance the reliability of relevance estimation [118,225,226].…”

Section: Number Of Data Sourcesmentioning

confidence: 99%

“…One limitation of MSFS is that it replies on combining sample similarity, which restricts its flexibility in handling small-sample data. To address this limitation we propose in [226] a general approach to systematically integrate different types of knowledge for Knowledge-Oriented multi-source feature selection, named KOFS. Figure 6.6 presents the major steps in the approach.…”

Section: A Framework Based On Rank Aggregationmentioning

confidence: 99%

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Spectral Feature Selection for Data Mining

Zhao¹,

Liu²

2011

Self Cite

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This book is for people interested in feature selection research. Feature selection is an essential technique for dimensionality reduction and relevance detection. In advanced data mining software packages, such as SAS Enterpriser Miner, SPSS Modeler, Weka, Spider, Orange, and scikits.learn, feature selection procedures are indispensable components for successful data mining applications. The rapid advance of computer-based high-throughput techniques provides unparalleled opportunities for humans to expand capabilities in production, services, communications, and research. Meanwhile, immense quantities of high-dimensional data keep on accumulating, thus challenging and stimulating the development of feature selection research in two major directions. One trend is to improve and expand the existing techniques to meet new challenges, and the other is to develop brand new techniques directly targeting the arising challenges. In this book, we introduce a novel feature selection technique, spectral feature selection, which forms a general platform for studying existing feature selection algorithms as well as developing novel algorithms for new problems arising from real-world applications. Spectral feature selection is a unified framework for supervised, unsupervised and semi-supervised feature selection. With its great generalizability, it includes many existing successful feature selection algorithms as its special cases, allowing the joint study of these algorithms to achieve better understanding and gain interesting insights. Based on spectral feature selection, families of novel feature selection algorithms can also be designed to address new challenges, such as handling feature redundancy, processing very large-scale data sets, and utilizing various types of knowledge to achieve multi-source feature selection. With the steady and speedy development of feature selection research, we sincerely hope that this book presents a distinctive contribution to feature selection research, and inspires new developments in feature selection. We have no doubt what feature selection can impact on the processing of massive, high-dimensional data with complex structure in the near future. We are truly optimistic that in another 10 years when we look back, we will be humbled by the accreted power of feature selection, and by its indelible contributions to machine learning, data mining, and many real-world applications.

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Section: Number Of Data Sourcesmentioning

confidence: 99%

Section: A Framework Based On Rank Aggregationmentioning

confidence: 99%

Spectral Feature Selection for Data Mining

Zhao¹,

Liu²

2011

Self Cite

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show abstract

“…Second, the base feature subsets are integrated together to generate a relatively complete feature subset. Data preprocessing or different feature selection algorithms can be used to generate multiple base feature subsets [16][17] [18][19] [20] . However, some of the previous research methods [15] [19] do not take into account the fitness of different feature selection algorithms to the data set.…”

Section: Introductionmentioning

confidence: 99%

Ensemble feature selection with adaptive weights

He,

Li,

Guo

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Most of the existing ensemble feature selection algorithms directly assign different feature selection algorithms the same weights or simply treat accuracy as weight and then vote for each feature. However, these research methods either ignored or did not fully consider the fitness of the feature selecttion algorithm to the data set. To deal with this challenge, we propose a new ensemble feature selection approach with adaptive weights. In detail, we use the softmax function to dynamically adjust the weight of the base feature selector according to its fitness to dataset in the k-round training process. We name it ensemble feature selection based on softmax function (EFS-BSF) algorithm. We demonstrate the superiority of the EFS-BSF approach over previous methods through mathematical analysis and experiments on multiple data sets.

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“…Feature selection have been an intense field of study in the recent years, gaining importance in parallel with the dimensionality reduction methods. Feature selection provides an advantage over dimensionality reduction methods because of its ability to distinguish and select the best available features in a data set [6][7][8][9][10]16]. This means that feature selection methods can be applied to both the original feature vectors and to the feature vectors that result from the application of dimensionality reduction methods.…”

mentioning

confidence: 99%

Ranked selection of nearest discriminating features

James

Dimitrijev

2012

Hum. Cent. Comput. Inf. Sci.

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Background: Feature selection techniques use a search-criteria driven approach for ranked feature subset selection. Often, selecting an optimal subset of ranked features using the existing methods is intractable for high dimensional gene data classification problems. Methods: In this paper, an approach based on the individual ability of the features to discriminate between different classes is proposed. The area of overlap measure between feature to feature inter-class and intra-class distance distributions is used to measure the discriminatory ability of each feature. Features with area of overlap below a specified threshold is selected to form the subset.

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An Integrative Approach to Identifying Biologically Relevant Genes

Cited by 13 publications

References 39 publications

Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining

Ensemble feature selection with adaptive weights

Ranked selection of nearest discriminating features

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