1. ANFIS presents a much better learning ability: for a similar network complexity, a much smaller convergence error is achieved, and although the convergence is slower the smallness of the error in ANFIS is able to compensate that fact.
2. ANFIS can achieve highly nonlinear mapping, far superior to MPL and other common linear methods of similar complexity.
3. ANFIS requires fewer adjustable parameters than those required in other Neural Network structures and, specifically, back propagation MPLs.
4. The ANFIS structure allows for parallel computation.
5. Finally, ANFIS presents two advantages exclusive to its method:
6. ANFIS networks present a well-structured knowledge representation.
7. ANFIS networks allow a better integration with other control design methods.
5.4 Recommendations for future work
• As a future direction, to refine the research, one may include variant software and process metrics in the model to reveal the relationships among them and to determine the most useful ones in defect prediction. Thus rather than dealing with a large set of software metrics, focusing on the most effective ones will improve the success rate in defect prediction studies.
• The input variables could be manipulated, either by decreasing or increasing the number of input variable, using various permutation and combination,in order to study and analyse their effect of the prediction model.
• Use of other membership function curves for the development of the ANFIS model and also for the evaluation of the parameter function values.
• Further, the rule extraction method may also be changed. Instead of subtractive clustering, one can use grid partitioning method. Also, comparative study of both can be carried out, bringing out the derits and demerits of these two rule extraction methods.
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