Cost-sensitive decision tree is a kind of decision tree which maximizes the sum of misclassification costs and test costs. Recently, with the explosive growth of data size, dirty data appears more frequently. In the process of cost-sensitive decision tree induction, dirty data in training datasets have negative impacts on selection of splitting attributes and division of decision tree nodes. Therefore, dirty data cleaning is necessary before classification tasks. Nevertheless, in practice, many users provide an acceptable threshold of data cleaning costs since time costs and expenses of data cleaning are expensive. Therefore, in addition to misclassification cost and test cost, data-cleaning cost is also an essential factor in cost-sensitive decision tree induction. However, existing researches have not considered data quality in the problem. To fill this gap, this study aims to focus on cost-sensitive decision tree induction on dirty data. Three decision tree induction methods integrated with data cleaning algorithms are presented. Experimental results demonstrate the effective of the proposed approaches.