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Application of Regression Tree Method for Different Data from Animal Science

PJZ_49_2_599-607

 

 

Application of Regression Tree Method for Different Data from Animal Science

Yusuf Koc1, Ecevit Eyduran1* and Omer Akbulut2

1Department of Animal Science, Agricultural Faculty, Igdir University, Igdir, Turkey

2Department of Actuarial Science, Faculty of Science, Ataturk University, Erzurum, Turkey

ABSTRACT

The aim of this study was to evaluate predictive performances of CHAID, Exhaustive CHAID, and CART regression tree methods for different combinations of parent node: child node in the data set regarding animal science. To achieve the aim, 1884 Mengali lambs were provided for predicting weaning weight from sex (male and female), birth type (single and twin), birth year (2005, 2006, 2007, 2008 and 2009), farm (Research station, Mastung, Quetta, and Noshki), birth weight, dam age, and dam weight. To choice the best regression tree method, goodness of fit criteria such as coefficient of determination (R2%), adjusted coefficient of determination (Adj-R2%), coefficient of variation (%), SD ratio, relative approximation error (RAE), Root Mean Square Error (RMSE), Pearson correlation between actual and predicted weaning weights were estimated for each combination. It was determined that CHAID algorithm constructed more suitable tree structures, biologically in comparison to Exhaustive CHAID and CART data mining algorithms. Consequently, it is recommended that the biological suitability of the constructed tree structure should be taken account together with estimating model quality criteria.


Article Information

Received 27 July 2016

Revised 10 August 2016

Accepted 09 September 2016

Available online 25 March 2017

Authors’ Contributions

The article is summarized from the first author’s (YK) MSc Thesis. EE and OA interpreted the data, drafted and carefully revised the manuscript. All authors read and approved the final manuscript.

Key words

CART, CHAID, Exhaustive CHAID, Regression tree, Data Mining, Weaning weight

DOI: http://dx.doi.org/10.17582/journal.pjz/2017.49.2.599.607

* Corresponding author: ecevit.eyduran@gmail.com

0030-9923/2017/0002-0599 $ 9.00/0

Copyright 2017 Zoological Society of Pakistan



INTRODUCTION

 

In animal breeding, it is very prominent to survey the interrelationship between body morphological characteristics and yield characteristics viz. meat, milk and egg. On the other hand, it is essential to ascertain the effect of non-genetic factors affecting the examined yield characteristics, and in the scope of indirect selection, it is notable to exhibit causal relationship between economic yield characteristics and their related quantitative characteristics. Several examples for the causal relationship are the prediction of body weight from body and testicular characteristics, and the prediction of milk yield from udder traits, and the prediction of the spermatological traits from testicular traits, and so on. The main objective in the relational mechanism is to gain better offspring from parent generation in yield traits.

In animal science, the causal relationships can be revealed by several statistical approaches, simple linear regression analysis, multiple linear regression analysis, use of factor analysis scores in multiple regression analysis, use of principal component analysis scores in multiple regression analysis, Path Analysis and Regression Tree Analysis (Khan et al., 2014). However, general linear models have been widely used in the identification of significant factors on yield traits (Eyduran et al., 2008).

Regression tree analysis, one of the analysis methods for evaluating animal data, is thought as an alternative to the above mentioned methods (Eyduran et al., 2008) and it is a non-parametric analysis method partitioning the population into relationships among independent variables playing major role for homogenous subsets and identifying curve linear and interaction in the explanation of the variability in yield trait, a dependent variable (Kayri and Boysan, 2008). The preferability of the decision tree method is due to having more advantageous in multicollinearity, outliers and missing data without needing any assumption on the distribution of independent variables (Mendeş and Akkartal, 2009).

In the construction of the decision or regression tree diagram, CART, CHAID and Exhaustive CHAID algorithms are non-parametric techniques applied for performing the statistical analysis of nominal, ordinal and scale (continuous) variables (Ali et al., 2015). When the dependent variable is scale, the constructed tree is called regression tree, otherwise classification tree (Camdeviren et al., 2005). Regression tree analysis based on the algorithms is employable instead of multiple linear regression, ridge regression, use of factor analysis scores or principal component analysis scores in multiple linear regression analysis. The classification tree analysis on the basis of the algorithms is a good alternative to logistic regression analysis and discriminant analysis.

Regression tree analysis on the basis of some data mining algorithms, C4.5, CART, CHAID, and Exhaustive CHAID is a non-parametric method used mostly in medicinal, engineering and industrial fields. However, although its applications in animal science are increasing with each passing time (Dogan, 2003; Grzesiak et al., 2003; Eyduran et al., 2008; Mendes and Akkartal, 2009; Bakir et al., 2009, 2010; Topal et al., 2010; Yakubu, 2012; Grzesiak and Zaborski, 2012; Çak et al., 2013; Eyduran et al., 2013a, b, c; Yilmaz et al., 2013; Orhan et al., 2016), a satisfying stress on using goodness of fit criteria was rare in measuring predictive performance of the algorithms. Besides, there is very limited number of the studies on comparatively testing the data mining algorithms; playing a key role in future selection studies (Ali et al., 2015). But, the effect of various parent and child nodes on the predictive performance of the data mining algorithms has not been investigated, so far by taking a basis for goodness of fit criteria. With these reasons, the aim of this study was to evaluate predictive performances of CHAID, Exhaustive CHAID, and CART regression tree methods for different combinations of parent node: child node in the data set regarding animal science.

 

MATERIALS AND METHODS

 

Animal material

With the intention of comparatively surveying in CHAID, Exhaustive CHAID and CART data mining algorithms, the data on 1884 indigenous Mengali sheep (936 males and 948 females) reared at four different farms in Pakistan were used. The input (independent) variables in the data were sex (male and female), birth type (single and twin), lambing year (2005, 2006, 2007, 2008 and 2009), farm (Research Station, Mastung, Quetta and Noshki), dam age (20 to 78 months) and dam weight (25 to 48 kg), respectively in the prediction of weaning weight, a dependent (output) variable. In order to determine the effect of applying different number of animals in parent child nodes on the prediction performance, twenty six combinations from 500:250 to 10:5 were measured in the predictive ability of the algorithms, CART, CHAID and Exhaustive CHAID.

Method

The structure of independent variables can be nominal, ordinal and scale in regression tree method (Mendes and Akkartal, 2009). As in the present study, regression tree method with the specification of the CHAID, Exhaustive CHAID and CART data mining algorithms are the convenient method that informs about the relationship between each of quantitative traits (such as body weight, milk yield and fleece weight) and nominal, ordinal and scale variables more than one. The trees constructed for dependent variable taking limited values is called classification trees, otherwise the trees obtained by the outcome variable taking unlimited values are called regression trees.

Recursively, CART (Classification and Regression Tree) creates a binary regression tree dividing a subset into 2 small subsets by the time homogenous subsets are attained in the tree, but CHAID algorithms create a regression tree that establishes recursively multi-splits until reaching up to maximum variance among subsets in the tree structure (Ali et al., 2015; Akin et al., 2016). Risk estimate is expressed as the variance within subsets in the regression tree construction.

Regression tree depths were adopted for the valued algorithms, CART (5) and CHAIDs (3). Examine the paper of Ali et al. (2015) for reaching more information concerning the algorithms.

Formulas of the goodness of fit (GOF) criteria measured for comparing predictive performance of the algorithms are below (Grzesiak and Zaborski, 2012):

 

Coefficient of Determination

COD.jpg 

 

Adjusted Coefficient of Determination

 

Adj.jpg 

 

Standard Deviation Ratio

 

SD%20Ratio.jpg 

 

Relative Approximation Error (RAE)

 

RAE.jpg 

 

Root Mean Square Error

RMSE.jpg 

 

Coefficient of Variation (%)

CV.jpgWhere,

Yi, the actual or observed weaning weight (kg) of ith lamb; Ŷi, the predicted weaning weight value of ith lamb; Ȳ, average of the actual weaning weight values of the Mengali lambs; Ɛi, the residual value of ith lamb; Ɛi average of the residual values; k, number of significant independent variables in the model; and n, total lamb number. The residual value of each lamb is expressed as Ɛi= Yi - Ŷi.

The Pearson correlation coefficients between the observed and predicted weaning weight values were estimated by using each of the algorithms. The most predictive algorithm estimates the highest values in r, R2, R2Adj and the lowest values in CV(%), SDRATIO, RAE, and RMSE, respectively (Ali et al., 2015). The regression trees were constructed by using IBM SPSS 23 software SPSS Inc., 2015. We followed the command order,

 

Table I.- Goodness of fit criteria for CHAID algorithm.

Parent node Child node RE

R2 (%)

RAE RMSE SDratio r

R2adj. (%)

CV (%)
500 250 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
480 240 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
460 230 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
440 220 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
420 210 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
400 200 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
380 190 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
360 180 0.951 52.235 0.249 0.996 0.706 0.708 50.102 6.222
340 170 0.976 52.979 0.244 0.975 0.691 0.723 52.184 6.092
320 160 0.966 51.482 0.246 0.983 0.697 0.717 51.429 6.140
300 150 0.955 52.034 0.244 0.977 0.693 0.721 51.981 6.105
280 140 0.955 52.034 0.244 0.977 0.693 0.721 51.955 6.105
260 130 0.953 52.135 0.244 0.976 0.692 0.722 52.071 6.098
240 120 0.926 53.491 0.240 0.962 0.682 0.731 53.368 6.011
220 110 0.926 53.491 0.240 0.962 0.682 0.731 53.401 6.011
200 100 0.902 54.696 0.237 0.950 0.673 0.739 54.575 5.933
180 90 0.814 59.116 0.225 0.902 0.640 0.769 59.015 5.636
160 80 0.801 59.769 0.224 0.895 0.635 0.773 59.631 5.592
140 70 0.791 60.271 0.222 0.889 0.630 0.776 60.156 5.555
120 60 0.721 63.787 0.212 0.849 0.602 0.799 63.707

5.303

100 50 0.761 61.778 0.218 0.872 0.618 0.786 61.696 5.448
80 40 0.687 65.495 0.207 0.829 0.588 0.809 65.397 5.178
60 30 0.663 66.700 0.203 0.814 0.577 0.817 66.614 5.085
40 20 0.571 71.321 0.189 0.756 0.536 0.844 71.222 4.721
20 10 0.532 73.280 0.182 0.730 0.517 0.856 73.173 4.558
10 5 0.510 74.385 0.178 0.714 0.506 0.863 74.320

4.460

 

Analyze > Classify > Tree…..in SPSS package program. Since our dependent variable (weaning weight) is scale, CART, CHAID and Exhaustive CHAID data mining algorithms are activated as growing methods in SPSS program to obtain a regression tree diagram. In the construction of the regression tree graph for each algorithm, the cross-validation value of 10 was employed.

 

RESULTS AND DISCUSSION

 

Performance of CART and both CHAID algorithms according to different parent: child node proportions specified for the study were compared in order to appoint some environmental factors that impress weaning weight well and summary results of goodness of fit criteria estimated for the algorithms are given in Tables I, II and III, respectively. This information is novel in literature. With being decreased the proportion from 500:250 through 10:5, their goodness of fit criteria were found better. The regression tree structure generated by CHAID algorithm was more interpretable than those constructed by CART and Exhaustive CHAID algorithms, causing over branching.

Following are results of Goodness of fit criteria for weaning weight

CHAID algorithm

When minimum parent: child node proportion was reached from 500:250 to 10:5, RE (0.992 to 0.510),

 

Table II.- Goodness of fit criteria for the Exhaustive CHAID algorithm.

Parent node Child node RE

R2 (%)

RAE RMSE SDratio r

R2adj. (%)

CV (%)
500 250 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
480 240 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
460 230 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
440 220 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
420 210 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
400 200 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
380 190 0.992 50.176 0.249 0.996 0.706 0.708 50.128 6.222
360 180 0.951 52.235 0.244 0.975 0.691 0.723 52.158 6.092
340 170 0.976 50.979 0.247 0.988 0.701 0.714 50.903 6.173
320 160 0.966 51.482 0.246 0.983 0.697 0.717 51.429 6.140
300 150 0.955 52.034 0.244 0.977 0.693 0.721 51.955 6.105
280 140 0.955 52.034 0.244 0.977 0.693 0.721 51.955 6.105
260 130 0.953 52.135 0.244 0.976 0.692 0.722 52.071 6.098
240 120 0.926 53.491 0.240 0.962 0.682 0.731 53.368 6.011
220 110 0.922 53.692 0.240 0.960 0.681 0.733 53.575 5.998
200 100 0.877 55.952 0.234 0.937 0.664 0.748 55.812 5.852
180 90 0.808 59.417 0.225 0.899 0.637 0.771 59.341 5.615
160 80 0.795 60.070 0.223 0.892 0.632 0.775 59.979 5.570
140 70 0.792 60.221 0.222 0.890 0.631 0.776 60.092 5.561
120 60 0.746 62.531 0.216 0.864 0.612 0.791 62.442 5.395
100 50 0.740 62.833 0.215 0.860 0.610 0.793 62.707 5.374
80 40 0.706 64.540 0.210 0.840 0.596 0.803 64.442 5.248
60 30 0.662 66.750 0.203 0.813 0.577 0.817 66.656 5.082
40 20 0.565 71.622 0.188 0.752 0.533 0.846 71.535 4.695
20 10 0.534 73.179 0.183 0.731 0.518 0.855 73.106 4.564
10 5 0.511 74.335 0.179 0.715 0.507 0.862 74.273

4.464

 

 

SD ratio (0.706 to 0.506), RAE (0.249 to 0.178), RMSE (0.996 to 0.506) and CV (%) (6.222 to 4.460) reduced for CHAID algorithm, whereas the rest goodness of fit criteria ascended (Table I). It was drawn attention from the statistical evaluation that Pearson correlation coefficient between observed and predicted weaning weight values depictured an increment from 0.708 to 0.863, which is an indicator of reducing the variance within nodes forming in the tree diagram. From Table I, it is obvious that no alteration was found in goodness of fit criteria of CHAID algorithm between parent: child nodes 380:190 and 500:250.

A regression tree diagram was constructed by the CHAID algorithm for parent: child node proportions 500:250, 480:240, 460:230, 440:220, 420:210:400:200 and 380:190, respectively. The tree diagram is depicted in Figure 1. All lambs in the Mengali population were split into five sub-subsets or populations (Nodes 1-5) as a result of significantly including birth weight, respectively. The heaviest lamb weaning weight of 17.614 kg was found for a subset of the lambs in Node 5 whose birth weight was greater than 3.800 kg.

The decision tree generated for only the parent: child node proportion 140:70 is illustrated in Figure 2. Node 0 was divided by birth weight (the most effective variable) into 8 subsets named Nodes 1-8, respectively. As birth weight increased from Node 1 to Node 8, weaning weight also increased (Fig. 2). Nodes 2, 3, 4 and 5 were effected by year factor (Adjusted P=0.000). Nodes 11 and 13 were divided by means of farm factor into two subsets (Nodes 19-20 and Nodes 21-22), respectively (Adjusted P=0.000). Node 9 was split by dam age into two subsets, respectively (Adjusted P=0.043). Node 15 was split by means of sex factor into two child subsets (Nodes 23 and 24), (Adjusted P=0.000).

Exhaustive CHAID algorithm

Table II demonstrated that RE (0.992-0.511), RAE (0.249-0.179), RMSE (0.996-0.715) and SD ratio (0.706-0.507), CV (%) (6.222-4.464), R2 (50.176-74.335%) and adjusted R2 (50.128-74.273%) estimates of Exhaustive CHAID tree-based algorithm from the parent-child node proportion 500-250 to 10:5 were made, which means that its predictive performance in weaning weight improved as result of reducing the variance within nodes. However, higher coefficients of determination, and Pearson coefficients (0.708-0.862) between actual and predicted weaning weight were obtained (Table II). For a good fit, an algorithm must have R2 greater than 70%. Goodness of fit criteria of the Exhaustive CHAID was found the definitely same with CHAID algorithm between the parent-child node proportions 500:250 and 380:190 (Tables I and II). But, at the proportions 300:150, 240:120 and 80:40, birth weight generated splits in succession in some branches of the regression tree diagram of Exhaustive CHAID algorithm. It could be suggested that use of CHAID and CART algorithms was better at the proportions. In agreement with our results, Tariq et al. (2012) determined

 

Table III.- Goodness of fit criteria for CART algorithm.

Parent Node

 

Child Node RE

R2 (%)

RAE RMSE SDratio r

R2adj (%)

CV(%)
500 250 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.463
480 240 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
460 230 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
440 220 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
420 210 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
400 200 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
380 190 1.022 48.667 0.253 1.011 0.719 0.697 48.593 6.332
360 180 0.920 53.790 0.253 1.011 0.719 0.697 48.538 6.332
340 170 0.920 53.790 0.240 0.959 0.680 0.733 53.701 5.991
320 160 0.863 56.653 0.240 0.959 0.680 0.733 53.701 5.991
300 150 0.863 56.653 0.232 0.929 0.659 0.753 56.553 5.803
280 140 0.863 56.653 0.232 0.929 0.659 0.753 56.553 5.803
260 130 0.863 56.653 0.232 0.929 0.659 0.753 56.553 5.803
240 120 0.829 58.361 0.232 0.929 0.659 0.753 56.553 5.803
220 110 0.829 58.361 0.227 0.910 0.645 0.764 58.292 5.686
200 100 0.802 59.717 0.227 0.910 0.645 0.764 58.292 5.686  
180 90 0.802 59.717 0.224 0.895 0.635 0.773 59.622 5.593  
160 80 0.743 62.681 0.224 0.895 0.635 0.773 59.622 5.593  
140 70 0.732 63.233 0.215 0.862 0.611 0.792 62.575 5.385  
120 60 0.712 64.238 0.214 0.855 0.606 0.795 63.150 5.343  
100 50 0.693 65.192 0.211 0.844 0.598 0.802 64.154 5.270  
80 40 0.672 66.247 0.208 0.832 0.590 0.807 65.099 5.200  
60 30 0.637 68.005 0.205 0.820 0.581 0.814 66.166 5.120  
40 20 0.582 70.767 0.199 0.798 0.566 0.825 67.919 4.983  
20 10 0.572 71.270 0.191 0.763 0.541 0.841 70.676 4.764  
10 5 0.565 71.621 0.189 0.756 0.536 0.844 71.169 4.724  

 

that the Exhaustive CHAID algorithm had longer operation time compared to CHAID algorithm. However, all the algorithms succeeded in reducing the variance within nodes.

 

CART algorithm

When different parent-child node proportions were examined (Table III), it was concluded that RE (1.022-0.565), RAE (0.253-0.189), RMSE (1.011-0.756) and SD ratio (0.719-0.536) CV (6.463-4.724%), R2 (48.667-71.621%) and adjusted R2 (48.593-71.169%) estimates of CART algorithm provided much better fit from 500:250 through 10:5 as also found in other algorithms. The result may be ascribed to reducing the variance within nodes (subsets) in weaning weight in the regression tree diagrams. Also, corresponding goodness of fit criteria such as coefficient of determination, adjusted coefficient of determination and Pearson correlation between predicted and actual weaning weight values increased under same conditions. It was found in the study that, in line with the other algorithms, CART reduced the variability within nodes or increased the variability among nodes in weaning weight, a response variable. In addition, some authors mentioned that SD ratio estimates of the data mining algorithms should be less than 0.40 for a good fit (Grzesiak et al., 2003; Grzesiak and Zaborski, 2012; Ali et al., 2015), which was in virtually agreement with those obtained for the algorithms in the study.

 

CONCLUSION

 

Weaning weight in farm animals played a considerable role on animal husbandry studies. In this respect, we intended in the study to comparatively examine the effect of different parent and child node proportions on predictive performance of CART, CHAID and Exhaustive CHAID data mining algorithms, and to observe the agreeableness of their tree constructions. All the algorithms had much better fit at parent and child node proportions from 500:250 to 10:5. For Mengali sheep data, CHAID algorithm generated more appropriate and deductive regression tree constructions. In literature, for example, the data mining algorithms can give more effective responses in the prediction of live body weight by morphological measurements, which are genetically correlated to the body weight (Khan et al., 2014; Ali et al., 2015).

As a result, it is expected that employing quantitative traits genetically correlated highly to a target trait like body weight with individual breeding coefficients and the data mining algorithms will serve a useful purpose in gaining superior animals for animal breeding studies. In other words, cut-off values of individual inbreeding coefficients in the regression tree diagrams formed by the tree-based algorithms might release information on degree of inbreeding depression in a flock.

 

Acknowledgment

 

The authors would like to thank Prof. Mohammad Masood Tariq and Dr. Abdul Waheed for allowing us to use their data in the study.

 

Conflict of interest statement

We declare that we have no conflict of interest.

 

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Pakistan Journal of Zoology (Associated Journals)

December

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