Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut

Akın, Melekşen; Eyduran, Ecevit; Reed, Barbara M.

doi:10.1007/s11240-016-1110-6

Cited by 71 publications

(36 citation statements)

References 15 publications

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“…“Pasadena” (Niedz et al, 2014); CaCl 2 .2H 2 O, MgSO 4 .7H 2 O and KH 2 PO 4 in Pyrus sp . (Wada et al, 2013) and Rubus idaeus L. (Poothong and Reed, 2014, 2015); KH 2 PO 4 , K 2 SO 4 and NH 4 NO 3 in Corylus avellana L. (Akin et al, 2017b) ; KH 2 PO 4 and MgSO 4 .7H 2 O in Prunus armeniaca Lam (Kovalchuk et al, 2017) in vitro culture have been reported. The use of salts as factors, in all of these studies, implies a problem of ion confounding, being difficult to identify exactly corresponding ion(s) impacting the parameter (Niedz and Evens, 2006).…”

Section: Discussionmentioning

confidence: 99%

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Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture

et al. 2018

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The aim of this study was to determine the effects of Murashige and Skoog (MS) salts on optimal growth of two pistachio rootstocks, P. vera cv. “Ghazvini” and “UCB1” using design of experiments (DOE) and artificial intelligence (AI) tools. MS medium with 14 macro—and micro-elements was used as base point and its concentration varied from 0 to 5 × MS concentrations. Design of experiments (DOE) software was used to generate a five-dimensional design space by categorizing MS salts into five independent factors (NH4NO3, KNO3, mesos, micros and iron), reducing the experimental design space from 3,125 to just 29 treatments. Typical plant growth parameters such as shoot quality (SQ), proliferation rate (PR), shoot length (SL), and some physiological disorders including shoot-tip necrosis (STN) and callus formation at the base of explants (BC) were evaluated for each treatment. The results were successfully modeled using neurofuzzy logic software. The model delivered new insights, by different sets of “IF–THEN” rules, pinpointing the key role of some ion interactions (SO42- × Cl−, K+ × SO42- × EDTA−, and Fe2+ × Cu2+ × NO3-) for SQ, PR, and SL, whilst physiological disorders (STN and BC) were governed mainly by independent ions as Fe2+ and EDTA−, respectively. In our opinion, the methodology and results obtained in this study is extremely useful to understand the effect of mineral nutrients on pistachio in vitro culture, through discovering new complex interactions among macro—and micro-elements which can be implemented to design new media of plant tissue culture and improve healthy plant micropropagation for any plant species.

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Section: Discussionmentioning

confidence: 99%

“…(Poothong and Reed, 2014, 2015), Corylus avellana L . (Hand et al, 2014; Akin et al, 2017a,b), Pear sp. (Reed et al, 2016) and Prunus armeniaca Lam (Kovalchuk et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture

et al. 2018

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“…Descriptive statistics of the input and output variables are given in Table 1. CART, CHAID, and Exhaustive CHAID are visual algorithms that create regression tree structures and analyze qualitative and quantitative data simultaneously. CHAID (Kass, 1980) and Exhaustive CHAID (Biggs et al, 1991) three-stage-data mining algorithms (merging, partitioning, and stopping) are tree-based algorithms that recursively use multi-way splitting to form homogenous subsets on the basis of Bonferroni adjustment until the differences between the actual and the predicted values in output variable are minimal (Orhan et al, 2016;Akin et al, 2016;Akin et al, 2017;Eyduran et al, 2016). A quantitative input variable in CHAID algorithms is converted into an ordinal variable (Orhan et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

et al. 2017

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-The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

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“…Therefore, these statistically significant interaction terms in the hybrid model further confirmed that the proposed hybrid approach, outperforms the pure logistic regression model and hence provides an alternative in handling bankcard response classifications. Age of the newest bankcard account x 4 Age of newest judgment public record item x 5 Dismissed bankruptcy public record within 24 months x 6 Total loan amount open mortgage accounts with update within 3 months x 7 Total balance open student loan accounts with update within 3 months x 8 Age of newest data last activity installment accounts paid as agreed x 9 Total balance closed bankcard accounts with update within 3 months x 10 Total past due amount installment accounts…”

Section: Model Comparisonmentioning

confidence: 99%

“…Chi-square automatic interaction detection (CHAID) analysis is an algorithm created by Gordon V. Kass in 1980 and it discovers relationships between independent variables and the categorical outcomes [7]. Chi-square tests are applied at each of the stages in building the CHAID tree and Bonferroni corrections are usually used to account for the multiple testing that takes place [8]. In general, CHAID analysis can be used for prediction and classification purposes as well as for detection of interactions between variables, such as diseases classification [9], financial distress prediction [10], and risk assessment [4] [11].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification

Wang

Stone³

2018

2018 5th International Conference on Systems and Informatics (ICSAI)

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Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model is that adding variable interactions may improve the performance of logistic regression. Theoretically, all possible interactions could be added in logistic regression and significant interactions could be identified by feature selection procedures. However, even the stepwise selection is very time-consuming when the number of independent variables is large and tends to cause the p >> n problem. On the other hand, using CHAID analysis for the detection of variable interactions has the potential to overcome the above-mentioned drawbacks. To demonstrate the effectiveness of the proposed hybrid model, it is evaluated on a real credit customer response data set. As the results reveal, by identifying potential interactions among independent variables, the proposed hybrid approach outperforms the logistic regression without searching for interactions in terms of classification accuracy, the area under the receiver operating characteristic curve (ROC), and Kolmogorov-Smirnov (KS) statistics. Furthermore, CHAID analysis for interaction detection is much more computationally efficient than the stepwise search mentioned above and some identified interactions are shown to have statistically significant predictive power on the target variable. Last but not least, the customer profile created based on the CHAID tree provides a reasonable interpretation of the interactions, which is the required by regulations of the credit industry. Hence, this study provides an alternative for handling bankcard classification tasks.

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Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut

Cited by 71 publications

References 15 publications

Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture

Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture

Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification

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