An empirical comparison of cost-sensitive decision tree induction algorithms

Decision tree induction is a widely used technique for learning from data, which first emerged in the 1980s. In recent years, several authors have noted that in practice, accuracy alone is not adequate, and it has become increasingly important to take into consideration the cost of misclassifying the data. Several authors have developed techniques to induce cost-sensitive decision trees. There are many studies that include pair-wise comparisons of algorithms, but the comparison including many methods has not been conducted in earlier work. This paper aims to remedy this situation by investigating different cost-sensitive decision tree induction algorithms. A survey has identified 30 cost-sensitive decision tree algorithms, which can be organized into 10 categories. A representative sample of these algorithms has been implemented and an empirical evaluation has been carried. In addition, an accuracy-based look-ahead algorithm has been extended to a new cost-sensitive look-ahead algorithm and also evaluated. The main outcome of the evaluation is that an algorithm based on genetic algorithms, known as Inexpensive Classification with Expensive Tests, performed better over all the range of experiments thus showing that to make a decision tree cost-sensitive, it is better to include all the different types of costs, that is, cost of obtaining the data and misclassification costs, in the induction of the decision tree.