A rapid learning algorithm for vehicle classification

Wen, Xuezhi; Shao, Ling; Xue, Yu; Fang, Wei

doi:10.1016/j.ins.2014.10.040

Cited by 447 publications

(163 citation statements)

References 39 publications

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“…Therefore, it is necessary to extend the method so that driver genes can be determined by not only somatic alterations but also other different types of molecular changes. Also, we can extend our aim of identifying drivers by some methods such as machine learning methods [47][48][49][50][51].…”

Section: Discussionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.

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Section: Discussionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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“…Williams et al [56] propose novel automated gender classification of subjects while engaged in running activity. The method with preprocessing steps using principle component analysis and AdaBoost classifier [57] achieves an accuracy of 87.8%. Nonverbal behavior is a very important part of human interactions.…”

Section: Dynamic Body Feature-based Gender Classificationmentioning

confidence: 99%

Human gender classification: a review

Zhuang

Long

et al. 2016

IJBM

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Abstract:The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application of gender classification research. Then, the development and framework of gender classification are described. We compare these state-of-the-art approaches, including vision-based methods, biological information-based methods, and social network informationbased methods, to provide a comprehensive review of gender classification research. Next we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for future work.

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“…Unlike GP where an individual is expressed in the form of a tree, an individual in GEP is represented by the Isometric linear symbols. GEP [10] has been successfully applied in problem solving [7], combinatorial optimization [11], real parameter optimization [12], evolving and modeling the functional parameters [13], classification [14,15], event selection in high energy physics [16].…”

Section: Introductionmentioning

confidence: 99%

Improved gene expression programming to solve the inverse problem for ordinary differential equations

Chen

et al. 2018

Swarm and Evolutionary Computation

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Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the model may be used to predict the future behavior of the physical system being modeled. Genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision.

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A rapid learning algorithm for vehicle classification

Cited by 447 publications

References 39 publications

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Human gender classification: a review

Improved gene expression programming to solve the inverse problem for ordinary differential equations

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