• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Plant Biotechnology
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• v.49 no.3
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• pp.240-249
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• 2022

Measuring chlorophyll fluorescence (CF) is a useful tool for assessing a plant's ability to tolerate abiotic stresses such as drought, waterlogging and high temperature. Korean ginseng is highly sensitive to water stress in paddy fields. To evaluate the possibility of non-destructively diagnosing waterlogging stress using chlorophyll fluorescence (CF) imaging techniques, we screened 57 ginseng accessions for waterlogging tolerance. To evaluate waterlogging tolerance among the 2-year-old Korean ginseng accessions, we treated ginseng plants with water stress for 25 days. The physiological disorder rate was characterized through visual assessment (an assigned score of 0-5). The physiological disorder rates of Geumjin, Geumsun and GS00-58 were lower than that of other accessions. In contrast, lines GS97-62, GS97-69 and GS98-1-5 were deemed susceptible. Root traits, chlorophyll content and the reduction rates decreased in most ginseng accessions. Further, these metrics were significantly lower in susceptible genotypes compared to resistant ones. All CF parameters showed a positive or negative response to waterlogging stress, and this response continuously increased over the treatment time among the genotypes. The CF parameter Fv/Fm was used to screen the 57 accessions, and the results showed clear differences in Fv/Fm between the susceptible and resistant genotypes. Susceptible genotypes had an especially low Fv/Fm value of less than 0.8, reflecting damage to the reaction center of photosystem II. It is concluded that Fv/Fm can be used as a CF parameter index for screening waterlogging stress tolerance in ginseng genotypes.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.2
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• pp.128-136
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• 2006

This study was conducted for developing the stability parameter in uncontrolled landfill by using a biomolecular investigation on the microbial community growing through leachate plume. Landfill J(which is in Cheonan) and landfill T(which is in Wonju) were chosen for this study among a total of 244 closed uncontrolled landfills. It addressed the genetic diversity of the microbial community in the leachate by 165 rDNA gene cloning using PCR and compared quantitative analysis of denitrifiers and methanotrophs with the conventional water quality parameters. From the BLAST search, genes of 47.6% in landfill J, and 32.5% in landfill T, respectively, showed more than 97% of the similarity where Proteobacteria phylum was most significantly observed. It showed that the numbers of denitrification genes, i.e. nirS gene and cnorB gene in the J site are 7 and 4 times higher than those in T site, which is well reflecting from a difference of site closure showing 7 and 13 years after being closed, respectively. In addition, the quantitative analysis on methane formation gene showed that J1 spot immediately bordering with the sources has the greatest number of methane formation bacteria, and it was decreased rapidly according to distribute toward the outer boundary of landfill. The comparative investigation between the number of genes, i.e. nirS gene, cnorB gene and MCR gene, md the conventional monitoring parameters, i.e. TOC, $NH_3-N,\;NO_3-N,\;NO_2-N,\;Cl^-$, alkalinity, addressed that more than 99% of the correlation was observed except for the $NO_3-N$. It was concluded that biomolecular investigation was well consistent with the conventional monitoring parameters to interpret their influences and stability made by leachate plume formed in downgradient around the uncontrolled sites.