Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and identify hidden relationships, there is a need to employ intelligent techniques, for example, data mining. Data mining is a process of knowledge discovery (Roiger & Geatz, 2003). There are a wide range of data mining techniques, one of which is decision trees. Decision trees, which can be used for the purposes of classifications and predictions, are a tool to support decision making (Lee et al., 2007). As a decision tree can accurately classify data and make effective predictions, it has already been employed for data analyses in many application domains. In this paper, we attempt to provide an overview of the applications that decision trees can support. In particular, we focus on business management, engineering, and health-care management. The structure of the paper is as follows. Firstly, Section 2 provides the theoretical background of decision trees. Section 3 then moves to discuss the applications that decision trees can support, with an emphasis on business management, engineering, and health-care management. For each application, how decision trees can help identify hidden relationships is described. Subsequently, Section 4 provides a critical discussion of limitations and identifies potential directions for future research. Finally, Section 5 presents the conclusions of the paper.
Decision trees are one of the most widely used classification and prediction tools. This is probably because the knowledge discovered by a decision tree is illustrated in a hierarchical structure, with which the discovered knowledge can easily be understood by individuals even though they are not experts in data mining (Chang et al., 2007). A decision tree model can be created in several ways using existing decision tree algorithms. In order to effectively adopt such algorithms, there is a need to have a solid understanding of the processes of creating a decision tree model and to identify suitability of the decision tree algorithms used. These issues are described in subsections below.
Processes of Model Development
A common way to create a decision tree model is to employ a top-down, recursive, and divide-and-conquer approach (Greene & Smith, 1993). Such a modelling approach enables the most significant attribute to be located at the top level as a root node and the least significant attributes to be located at the bottom level as leave nodes (Chien et al., 2007). Each path between the root node and the leave node can be interpreted as an ‘if-then’ rule, which can be used for making predications (Chien et al., 2007; Kumar & Ravi, 2007).
To create a decision tree model on the basis of the above-mentioned approach, the modelling processes can be divided into three stages, which are: (1) tree growing, (2) tree pruning, and (3) tree selection.
The initial stage of creating a decision tree model is tree growing, which includes two steps: tree merging and tree splitting. At the beginning, the non-significant predictor categorises and the significant categories within a dataset are grouped together (tree merging). As the tree grows, impurities within the model will increase. Since the existence of impurities may result in reducing the accuracy of the model, there is a need to purify the tree. One possible way to do it is to remove the impurities into different leaves and ramifications (tree splitting) (Chang, 2007).
Tree pruning, which is the key elements of the second stage, is to remove irrelevant splitting nodes (Kirkos et al., 2007). The removal of irrelevant nodes can help reduce the chance of creating an over-fitting tree. Such a procedure is particularly useful because an over-fitting tree model may result in misclassifying data in real world applications (Breiman et al., 1984).
The final stage of developing a decision tree model is tree selection. At this stage, the created decision tree model will be evaluated by either using cross-validation or a testing dataset (Breiman et al., 1984). This stage is essential as it can reduce the chances of misclassifying data in real world applications, and consequently, minimise the cost of developing further applications.
Key Terms in this Chapter
Classif ication: An allocation of items or objects to classes or categories according to their features.
Fault Diagnosis: An action of identifying a malfunctioning system based on observing its behaviour.
Healthcare Management: The act of preventing, treating and managing illness, including the preservation of mental and physical problems through the services provided by health professionals
Customer Relationship Management: A dynamic process to manage the relationships between a company and her customers, including collecting, storing and analysing customers’ information
Decision Tree: A predictive model which can be visualized in a hierarchical structure using leaves and ramifications.
Prediction: A statement or a claim that a particular event will happen in the future.
Data Mining: Also known as knowledge discovery in database (KDD), which is a process of knowledge discovery by analysing data and extracting information from a dataset using machine learning techniques
Decision Tree Modelling: The process of creating a decision tree model.
Fraud Detection Management: The detection of frauds, especially in those existing in financial statements or business transactions so as to reduce the risk of loss
Attributes: Pre-defined variables in a dataset.