Paper: A Fault Forecasting Approach Using Two-Dimensional Optimization (TDO) (ICLR-2024)

Reviewer comment (Experiments):
While the paper does explore GA, PSO, and WOA for the automotive fault detection problem, it lacks a comparison with other existing methods or state-of-the-art techniques. Providing such a comparative study would have offered more context to the results. Also, adding references to the previous works/results in the domain would add to the paper results validation.

Author response:
Many thanks for the valuable comments. The four bullet points you mentioned are quite right and pretty crucial for our study. Thus, to answer your questions, we are running our approach on other datasets together with other ML algorithms to assess our approach in different contexts. We are updating the paper with a section to show the comparison results. We are also describing how we reached the optimal parameters illustrated in the tables.  Regarding the statistics about the dataset, we are pretty limited in providing such detailed information since the data is quite sensitive for the company we are collaborating with. However, we try to add more information that can answer your concerns and follow the company regulations.

Reviewer comment (Experiments):
The paper could have benefited from a more comprehensive analysis using multiple datasets or a more diversified dataset. Relying on a single dataset might limit the generalizability of the method and findings.

Author response:
Many thanks for the valuable comments. The four bullet points you mentioned are quite right and pretty crucial for our study. Thus, to answer your questions, we are running our approach on other datasets together with other ML algorithms to assess our approach in different contexts. We are updating the paper with a section to show the comparison results. We are also describing how we reached the optimal parameters illustrated in the tables.  Regarding the statistics about the dataset, we are pretty limited in providing such detailed information since the data is quite sensitive for the company we are collaborating with. However, we try to add more information that can answer your concerns and follow the company regulations.

Reviewer comment (Experiments):
The paper lacks in describing the statistics of the dataset in the findings; it didn't provide comprehensive results that include the percentage/number of useful/selected features/tuples.

Author response:
Many thanks for the valuable comments. The four bullet points you mentioned are quite right and pretty crucial for our study. Thus, to answer your questions, we are running our approach on other datasets together with other ML algorithms to assess our approach in different contexts. We are updating the paper with a section to show the comparison results. We are also describing how we reached the optimal parameters illustrated in the tables.  Regarding the statistics about the dataset, we are pretty limited in providing such detailed information since the data is quite sensitive for the company we are collaborating with. However, we try to add more information that can answer your concerns and follow the company regulations.

Reviewer comment (Reproducibility):
The authors mention parameterizing the three optimization algorithms as per values shown in "Table 1", yet they didn't explain how these optimal values were derived. Understanding the selection process for these parameters is essential for reproducibility and validation of the results.

Author response:
Many thanks for the valuable comments. The four bullet points you mentioned are quite right and pretty crucial for our study. Thus, to answer your questions, we are running our approach on other datasets together with other ML algorithms to assess our approach in different contexts. We are updating the paper with a section to show the comparison results. We are also describing how we reached the optimal parameters illustrated in the tables.  Regarding the statistics about the dataset, we are pretty limited in providing such detailed information since the data is quite sensitive for the company we are collaborating with. However, we try to add more information that can answer your concerns and follow the company regulations.