Decision Tree Induction Techniques Research Articles

Meteorological Data Warehousing and Analysis for Supporting Air Navigation

Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data warehouse (DW) based on a new hybrid logical schema, concerning the assimilation and maintenance of historical meteorological data from all operating airports in Greece, along with data in the Greek Flight Information Region related to flight delays and cancellations. SQL is used for querying these data and makes them easily accessible and manageable. The data from the DW are collected and used as training data for the induction of predictive models. In this study, the prediction problem is cast as a classification task, and different decision tree induction techniques are applied to build accurate models that allow flexible scheduling and planning for the minimization of waiting time and inconvenience of passengers.

Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon

AbstractIn the Amazon, there are soils associated with continued human occupation known as “archeological black earth” (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify the most relevant variables for the proposed model: (i) correlation‐based feature selection, (ii) the χ2 test, (iii) the Wrapper method, and (iv) no feature selection. The decision tree induction technique available in the Weka software was selected for data classification. Soils under cacao and coffee cultivation tend to accumulate more C when compared with soils located at bean crops, pasture, or forest land use systems. Land use and sand content were among the most important variables for the prediction of soil C stock in ABE. Furthermore, the use of a decision tree was effective at predicting soil C stocks for these soils because it enables the creation of models with high accuracy rates of 83, 74, and 81% (using seven, seven, and four rules at depths of 0.00–0.05, 0.05–0.10, and 0.10–0.20 m, respectively).

Classification of soil respiration in areas of sugarcane renewal using decision tree

The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soil moisture > potential acidity.

Wheat Yield Assessment using Decision Tree Algorithms

Prediction of agriculture yield with the help of data mining tools and techniques is one of the emerging research domains from past couple of years. This paper focuses on studying the effects of rainfall and relative humidity on three different stages of wheat crop and thus generating rules about crop yield using decision tree induction technique. A comparison of different classification algorithms is also done to discover the best rules about wheat crop yield with maximum accuracy and least errors. These study findings will help the farmers in future to predict their wheat crop yield in advance before crop growing season.

CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES

ABSTRACT Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decision-making process. The aim of this study was to model the influence of climate and physico-chemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined four approaches to select these features: no selection, Correlation-based Feature Selection (CFS), Chi-square test (χ2) and Wrapper. To classify the data, we used the decision tree induction technique available in the Weka software 3.6. This data mining technique allowed us to create a classification model for the initial development of eucalyptus. From this model, one can predict new cases in different production classes, in which the individual wood volume (IWV) and the diameter at breast height (DBH) are crucial features to predict the growth of Eucalyptus urograndis, in addition to the presence of chemical soil components such as: magnesium (Mg+2), phosphorus (P), aluminum (Al+3), potassium (K+), potential acidity (H + Al), hydrogen potential (pH), and physical attributes such as soil resistance to penetration and related to climate, such as minimum temperature.

Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach

BackgroundImproving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. MethodsDecision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. ResultsThe child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR= 1.46, (95% CI [1.22. 1.75]), maternal education (AOR= 1.40, 95% CI [1.11, 1.81]), family planning (AOR= 1.21, [1.08, 1.43]), preceding birth interval (AOR= 4.90, [2.94, 8.15]), presence of diarrhea (AOR= 1.54, 95% CI [1.32, 1.66]), father's education (AOR= 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR= 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR= 1.42, [1.01–1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. ConclusionIn this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.

Ethiopic maternal care data mining: discovering the factors that affect postnatal care visit in Ethiopia

BackgroundImproving maternal health and reducing maternal mortality rate are key concerns. One of the eight millennium development goals adopted at the millennium summit, was to improve maternal health in Ethiopia. This leads towards discovering the factors that hinder postnatal care visit in Ethiopia.MethodsIn this research, knowledge discovery from data (KDD) was applied to identify the factors that hinder postnatal care visits in Ethiopia. Decision tree (using J48 algorithm) and rule induction (using JRip algorithm) techniques were applied on 6558 records of Ethiopian demographic and health survey data. To construct essential target dataset attributes exploratory data analysis with frequency diagram is performed, missing value was filled and noisy value was corrected. Also the data are preprocessed using business and data understanding with detail statistical summary.ResultJ48 (93.97 % accuracy) and JRip (93.93 % accuracy) identifies places of delivery, assistance of health delivery professional, prenatal care health professional and age are the determinant factors. However, residence places also taken into consideration.ConclusionsIn this study, encouraging results were obtained by employing both decision tree and rule induction techniques. The rules generated by J48 and JRip algorithms are much understandable to explain the outcome easily. Thus, the result obtained highly supportive to construct, evaluate and update advertising and promotional maternal health policies. It is better to create a generic model with more coverage in terms of economic, demographic, social and genetic factors so as to integrate the result with knowledge based system.

Soybean Productivity Modelling using Decision Tree Algorithms

Data mining applications in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. In the present study an attempt has been made to study the influence of climatic parameters on soybean productivity using decision tree induction technique. The findings of Decision tree were framed into different rules for better understanding by the end users. The study findings will help the researchers, policy makers and farmers in predicting/forecasting the crop yield in advance for market dynamics. Keywords— Decision tree, crop productivity, ID3 algorithm, climatic factors

Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis

A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretisation prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated that uses multiple decision trees, i.e., a decision forest, and a majority voting strategy to give predictions (GPForest). The methods were applied to QSAR (quantit...

Exploring Decision Rules for Sellers in Business-to-Consumer(B2C) Internet Auctions

The recent growth of business-to-consumer (B2C) Internet auctions challenges researchers to develop empirically-sound explanations of critical factors that allow merchants to earn price premiums in these auctions. The absence of a comprehensive model of Internet auctions leads us to conduct an exploratory study to elucidate and rank critical factors that lead to price premiums in Internet auctions. We employ Classification and Regression Trees (CART), a decision-tree induction technique, to analyze data collected in a field study of eBay auctions. Our analysis yields decision trees that visually depict noteworthy factors that may lead to price premiums and that indicate the relative importance of these factors. We find shipping cost, reputation, initial bid price, and auction ending time as the factors most predictive of price premiums in B2C Internet auctions.

Decision Tree Based Qualitative Analysis of Operating Regimes in Industrial Production Processes

The qualitative analysis of complex process systems is an important task at the design of control and process monitoring algorithms. Qualitative models require interpretable description of the operating regimes of the process. This work shows a novel approach to discover and isolate operating regimes of process systems based on process models, time series analysis, and decision tree induction technique. The novelty of this approach is the application of time series segmentation algorithms to detect the homogeneous periods of the operation. Advanced sequence alignment algorithm used in bioinformatics is applied for the calculation of the similarity of the process trends described by qualitative variables. Decision tree induction is applied for the transformation of this hidden knowledge into easily interpretable rule base to represent the operation regions of the process. The whole methodology is applied to detect operating regimes of an industrial fixed bed tube reactor.

Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints

Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.