New Classification of Plasmodiophora brassicae Races Using Differential Genotypes of Chinese Cabbage
-
- 한국균학회소식:학술대회논문집
- /
- 2015.05a
- /
- pp.28-28
- /
- 2015
Clubroot disease caused by Plasmodiophora brassicae induces severe losses of cruciferous vegetables worldwide. To control clubroot of Chinese cabbage, many CR (clubroot resistance) F1 hybrid cultivars have been bred and released in Korea, China and Japan. In this study, we determined the race of P. brassicae 12 field isolates, which collected from 10 regions in Korea, using Williams' differential varieties including two cabbage ('Jersey Queen', 'Badger Shipper') and two rutabaga ('Laurentian', 'Whilhelmsburger'). By Williams' differential varieties, 12 clubroot pathogens were assigned into one (GN2), two (HS and YC), two (HN1 and HN2), three (DJ, KS and SS) and four (GS, GN1, JS and PC) isolates for races 1, 2, 4, 5 and 9, respectively. In addition, the degree of resistance of 45 CR cultivars that were from Korea, China and Japan was tested with the 12 isolates. The 45 CR cultivars of Chinese cabbage were differentiated into three genotypes according to their resistance responses. Even though the 12 P. brassicae isolates were same race by Williams' differential varieties, three CR genotypes showed different resistance response to the isolates. These results indicate that races of P. brassicae by Williams' differentials were not related with resistance of CR cultivars, and three CR genotypes represented qualitative resistance to the P. brassicae isolates. CR genotype I including 'CR-Cheongrok' showed resistance to GN1, GN2, JS, GS, HS, DJ and KS isolates and susceptibility to YC, PC, HN1, HN2 and SS isolates. And CR genotype II such as 'Hangkunjongbyungdaebaekchae' was resistant to GN1, GN2, JS, GS, HS, YC, PC and HN1 and susceptible to DJ, KS, SS and HN2. CR genotype III including 'Chunhajangkun' and 'Akimeki' represented resistance to 10 isolates except for SS and HN2 isolates. Based on these results, we selected 'CR-Cheongrok', 'Hangkunjongbyungdaebaekchae', and 'Chunhajangkun' as a representative cultivar of three CR genotypes and 'Norangkimjang' as a susceptible cultivar. Furthermore, we investigated the resistance of 15 lines of Chinese cabbage, which were provided by seed companies, to 11 isolates except for HN1 of P. brassicae. The results showed that three lines were susceptible to all the tested isolates, whereas five, four, and three lines represented the similar responses corresponding to the CR genotypes I, II, and III, respectively; there is no line of Chinese cabbage showing different resistance patterns compared to three CR genotypes. In particular, line 'SS001' showing resistance responses of CR genotype II was a parent of 'Saerona' that have been commercialized as a CR
Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.