Maize Genetics Cooperation Newsletter vol 84 2010

Please Note: Notes submitted to the Maize Genetics Cooperation Newsletter may be cited only with consent of authors.

 

ROSARIO, ARGENTINA.

Facultad de Ciencias Exactas, Ingenier’a y Agrimensura - UNR.

Centro Internacional Franco Argentino de Ciencias de la Informaci—n y de Sistemas - CONICET

Application of error correcting codes for heterotic group assignation.

--Ornella, L; Tapia, E

Introduction of exotic maize (Zea mays L.) into breeding programs may enhance genetic variability and lead to greater progress from selection.  However, the pool of available exotic germplasm is large and diverse, making choices of potential parents difficult. Two major heterotic group-classification methods are currently used widely across the world. The traditional method uses specific combining ability with some line-pedigree information and/or field hybrid-yield information to assign a maize line to a heterotic group (Hallauer and Miranda, Quantitative Genetics in Maize Breeding, 2nd ed. Iowa State Univ. Press, Ames, IA, 1988). Another method employs various molecular markers to compute genetic similarity (GS) or genetic distance (GD) in order to assign maize lines to different heterotic groups (Mohammadi and  Prasanna, Crop Sci. 43:1235–1248, 2003). However, the results of these associations are still inconsistent (dos Santos Dias et al., Genet. Mol. Res. 3: 356-368, 2004).

Machine learning is an emerging scientific discipline concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases (Witten and Eibe, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed.  Morgan Kaufmann, San Francisco, 2005). In particular, supervised learning algorithms allows for deducing a function from training data. The training data consist of pairs of input objects (typically vectors of features) and desired outputs i.e. the class (Witten and Eibe, 2005).

We conjecture that traditional distance-based methods currently available do not capture the non-linear relationship between parental molecular data and progeny performance and that such hindrance can be overcome by supervised learning algorithms. Among them, support vector machines (SVMs) have shown high generalization abilities and have become very popular in the last few years (Rifkin and Klautau, JMLR 5:101-114, 2004). However, they are binary classifiers and a combination scheme is necessary to extend SVMs for problems with more two classes (Rifkin and Klautau, JMLR 5:101-114, 2004). In this work we explore the performance of the recently introduced class of ECOC-SVM (Error Correcting Output Coding-Support Vector Machine) classifiers, based on recursive error correcting codes of communication theory (Tapia et al., LNCS 3541:108–117, 2005), in heterotic group assignation. As a control we used four (4) Native multiclass classifiers: Naive Bayes (John and Langley, 11th Conf. on Uncertainty in A, 338–345, 1995), Bayes Network (Friedman et al., Mach Learn. 29:131–163, 1997), Decision Tree J48 (Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA., 1993) and Logistic Model Trees or just Simple Logistic (Landwehr et al., Mach Learn. 161–205, 2005).  We also report the performance of the ensemble method using as a base classifier Naive Bayes and J48 (Witten and Eibe, 2005).

Classifiers were evaluated with two datasets: Morales dataset, involving 26 inbreeds of temperate germplasm clustered into four heterotic groups by topcross (Eyherabide et al., Plant Breeding: The Arnel R. Hallauer International Symposium, Blackwell Publishing, pp. 352–379, 2006) and characterized by 42 attributes derived from 21 microsatellites (MNL 79:36-37, 2005); and Xia dataset,  comprising 73 inbreeds of tropical germplasm clustered in 8 heterotic groups and characterized  by 166 attributes derived from 83 microsatellites (Xia et al., Crop Sci. 4:2230-2237, 2004).

The decomposition method of ECOC employs a binary matrix of order  to convert a k-multiclass problem into n binary tasks () (Dietterich and Bakiri, JAIR 2:263-286, 1995); this allows us to explore the performance of matrices with n = [2,3,É, 7] for Morales Data, and n = [3, 16, 29,É , 120 ]  for Xia data.

The predictive power of proposed algorithms was evaluated by means of CohenŐs Kappa coefficient (Landis and Koch, Biometrics 33: 159–174, 1977) exhibited across 30 Montecarlo runs of stratified 10-Fold Cross Validation (CV) experiments (Kirchner et al., Comput Electron Agric. 42:111–127.2004). The choice of the Kappa coefficient was motivated by its ability to better measure the agreement between binary inter-annotators than the traditional error rate. The former takes into account chance agreements and it is well suited for unequal class distribution datasets then traditional error rate (Kirchner et al., 2004).

Figures 1 and 2 show the performance of the 7 classifiers on Morales and Xia dataset respectively.

Results of ECOC codes corresponds to kappa values obtained with codes that, with the lowest number of columns reached the best kappa value (n = 55 for Xia Data and n = 7 for Morales data).

Although some statistical test is needed in order to support the significance of the better or poorer performance of classifiers, it can be seen from visual inspection of both boxplots that ensembles (ECOC codes) outperform the performance of most native classifiers. The only exception is simple logistic, which outperforms ECOC-SMO ensemble in Morales data; however it remains to explore the optimizations of SVM parameters, which is reported to significantly improve the final performance of the ensemble (Rifkin and Klautau, 2004).

It is reported that the aim of the classifier ensembles is to take advantage of the individual classifiersŐ capabilities by selecting or weighting their individual decisions (Dietterich, LNCS 1857: 1-15, 2000). It also can be seen from both boxplots that ensembles (ECOC codes) of Decision tree and Na•ve Bayes did improve the performance of the single classifier (Figs 1 and 2).

 

 

 

 

 

 

Figure 1- Morales data. Boxplots of the CohenŐs Kappa coefficient in 30 Montecarlo runs of 10-Fold CV experiments.

ECOC - xx  Error correcting output code + base classifier: NB -  Na•ve Bayes, SVM - Support vector machine,  J48 -Decision Tree J48.

 

 

 

 

Figure 2- Xia data. Boxplots of the CohenŐs Kappa coefficient in 30 Montecarlo runs of 10-Fold CV experiments.

ECOC - xx  Error correcting output code + base classifier: NB -  Na•ve Bayes, SVM - Support vector machine,  J48 -Decision Tree J48.

 

 

 

 

 

 

Finally, although results obtained do not allow molecular markers to replace field essays (top cross or diallel) in heterotic group assignment[1], there is strong evidence that using data with more training instances could generate successful classifiers. Moreover, the potential impact, in time and money, on crop sustainability makes our research worth to try: while traditional genetic breeding requires expensive  fields test and a time scale in the order of years for obtaining an heterotic assignment, in our proposed framework costs are significantly lower and the time scale is in the order of weeks, two weeks for growing an small plant plus a week to obtain molecular data and a couple of days for computational  analysis.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



[1] Kappa values ranging between 0.41-0.60 indicate a moderate agreement between observed and predicted data whereas values below 0.20 indicate only a slight agreement (Landis & Koch, 1977).