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Using non-additive Measure for Optimization-Based nonlinear Classification

American Journal of Operations Research 2012年第3期2卷 364-373页

作者： Nian Yan Zhengxin Chen Yong Shi Zhenyuan Wang Guimin Huang College of Information Science and Technology University of Nebraska at Omaha Omaha NE USA Department of Mathematics University of Nebraska at Omaha Omaha NE USA 68182 Guilin University of Electronic Technology Guilin China

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2n – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are relatively small number of training cases available (mn-1). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered.

关键词： nonlinear Programming nonlinear Classification non-additive Measure Choquet Integral Support Vector Machines

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Dynamic Patterns of Gene Expression Additivity and Regulatory Variation throughout Maize Development

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Molecular Plant 2019年第3期12卷 410-425页

作者： Peng Zhou Candice N.Hirsch Steven P.Briggs Nathan M.Springer Department of Plant and Microbial Biology University of MinnesotaSaint PaulMN 55108USA Department of Agronomy and Plant Genetics University of MinnesotaSaint PaulMN 55108USA Division of Biological Sciences University of CaliforniaSanDiegoLaJollaCA92093USA

Gene expression variation is a key component underlying phenotypic variation and heterosis. Transcriptome profiling was performed on 23 different tissues or developmental stages of two maize inbreds, B73 and Mo17, as well as their hybrid. The obtained large-scale datasets provided opportunities to monitor the developmental dynamics of differential expression, additivity for gene expression, and regulatory variation. The transcriptome can be divided into .30 000 genes that are expressed in at least one tissue of one in bred and an additional ~10 000 “silent” genes that are not expressed in any tissue of any genotype, 90% of which are non-syntenic relative to other grasses. Many (.74%) of the expressed genes exhibit differential expression in at least one tissue. However, the majority of genes with differential expression do not exhibit consistent differential expression in different tissues. These genes often exhibit tissue-specific differential expression with equivalent expression in other tissues, and in many cases they switch the directionality of differential expression in different tissues. This suggests widespread variation for tissue-specific regulation of gene expression between the two maize inbreds B73 and Mo17. Nearly 5000 genes are expressed in only one parent in at least one tissue (single parent expression) and 97% of these genes are expressed at mid-parent levels or higher in the hybrid, providing extensive opportunities for hybrid complementation in heterosis. In general, additive expression patterns are much more common than non-additive patterns, and this trend is more pronounced for genes with strong differential expression or single pare nt expressi on. There is relatively little evidence for non-additive expression patterns that are maintained in multiple tissues. The analysis of allele-specific expression allowed classification of cis. and trans-regulatory variation. Genes with c/s-regulatory variation often exhibit additive expression and tend to have more consistent regulatory variation th

关键词： maize gene expression heterosis differential expression non-additive expression allele-specific expression

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Heterosis, Combining Ability and Gene Action for Yield in Bottle Gourd

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American Journal of Plant Sciences 2020年第5期11卷 642-652页

作者： Akm Quamruzzaman M. M. R. Salim L. Akhter M. M. Rahman M. A. Z. Chowdhury Olericulture Division Horticulture Research Centre Bangladesh Agricultural Research Institute Gazipur Bangladesh Bangladesh Agricultural Research Council Farmgate Dhaka Bangladesh

The experiment was conducted at the experimental field of Olericulture Division, Horticulture Research Centre (HRC), Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh during the winter season of 2018-2019 to study the genetic architecture of yield in a seven parent half diallel cross of bottle gourd. The values of mean square for GCA (general combining ability) and SCA (specific combining ability) were highly significant which suggested the presence of both additive and non-additive genetic variance in the population. But the higher magnitude of GCA compared to SCA indicated predominance of additive genetic variance. In most of the cases, the cross between poor and poor parents showed positive SCA effect for fruit yield, which indicated the higher yield. The estimates of significant positive better parent heterosis ranged from 6.27 to 49.72 percent. Analysis of genetic components of variation suggested that additive components were more important in the inheritance of fruit yield. This character was observed being controlled by two to three pairs of genes or groups of genes. Narrow sense heritability was 23 percent indicating probability of selection in generations. The graphical analysis also indicated wide genetic diversity among the parents.

关键词： Bottle Gourd Combining Ability Heterosis Genetic Architecture additive and non-additive Gene Actions

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