Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems^†

Round 1

Reviewer 1 Report

The core of the manuscript is the so-called Hybrid Evolutionary Fuzzy Classification Algorithm (HEFCA), which is composed of a fuzzy classifier and one of the following approaches as neural network, decision tree or random forest serving as auxiliary classifier. However, it is not clearly described, when which approach is used. Generally, more details are needed in the final paper.

In general, the citations are quite obsolete. They need an update and just after that it is possible to compare the proposed classification method with other ones. Otherwise it look more as a casual mixture of several classification methods.

 

Other comments and questions:

How to determine a correct level of granulation?

What is the Michigan part operator?

The section 2.2 and 2.3 do not have any citation and need a more detailed explanation.

The quality of English is quite good but it still needs a careful reading and correction.

Author Response

Dear Reviewer,

Considering your valuable suggestions, the following changes were made to the manuscript:

1. The description of the fuzzy classification algorithm HEFCA was improved, it was mentioned that the usage of several granulation levels was proposed in earlier studies.
2. New citations are added in the introduction, sections 2.2 and 2.3.
3. The description of the Michigan part is provided.
4. The flowchart of the CBV algorithm was added.

Reviewer 2 Report

1. Results: Recommend to be Major revisions

This paper proposes a new voting procedure for combining the fuzzy logic based classifiers and other classifiers called confidence-based voting. The obtained results demonstrate the efficiency of the proposed technique, as it allows both improving the classification accuracy and explaining the decision making process.

It is with some merits for Algorithms. However, it requires some major revisions.

Firstly, the abstract is disorganized, it should be refined to precisely illustrate what authors have done in this paper within 200 words.

Secondly, for Section 1, authors should provide the comments of the cited papers after introducing each relevant work. What readers require is, by convinced literature review, to understand the clear thinking/consideration why the proposed approach can reach more convinced results. This is the very contribution from authors. In addition, authors also should provide more sufficient critical literature review to indicate the drawbacks of existed approaches, then, well define the main stream of research direction, how did those previous studies perform? Employ which methodologies? Which problem still requires to be solved? Why is the proposed approach suitable to be used to solve the critical problem? We need more convinced literature reviews to indicate clearly the state-of-the-art development. And very importantly, authors always have to write a paragraph saying: “The rest of the paper is organised as follows. Section 2 contains the literature review. Section 3 contains the methodology (method). Section 4 contains the results. Section 5 contains the conclusions and policy implications”. So, the reader knows what’s coming next.

For Section 2, authors should introduce their proposed research model/framework more effective, i.e., some essential brief explanation vis-à-vis the text with a total research flowchart or framework diagram for each proposed algorithm to indicate how these employed models are working to receive the experimental results. It is difficult to understand how the proposed approaches are working.

For Section 3, authors should also conduct some statistical test to ensure the superiority of the proposed approach, i.e., how could authors ensure that their results are superior to others only based on Tables 4 and 5? In addition, authors also have to provide some insight discussion of the results with Tables 4 and 5. Please refer the following references.

Zhang, Z.-C., Hong, W.-C., Li, J. Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access 2020, 8, 14642-14658.

Hong, W.-C.; Li, M.W.; Geng, J.; Zhang, Y. Novel chaotic bat algorithm for forecasting complex motion of floating platforms. Applied Mathematical Modelling 2019, 72, 425-443.

Zhang, Z.-C.; Hong, W.-C. Electric load forecasting by complete ensemble empirical model decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics 2019, 98, 1107-1136.

Dong, Y., Zhang, Z., and Hong, W.-C. A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies 2018, 11(4), 1009.

Hong, W.-C., Dong, Y., Lai, C.-Y., Chen, L.-Y., Wei, S.-Y. SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting. Energies 2011, 4(6), 960-977.

Kundra, H.; Sadawarti, H. Hybrid algorithm of cuckoo search and particle swarm optimization for natural terrain feature extraction. Research Journal of Information Technology 2015, 7, 58-69.

Author Response

Dear Reviewer,

Considering your valuable suggestions, the following changes were made to the manuscript:

1. The abstract was revised and extended.
2. New citations are added in the introduction and section 2.2, 2.3.
3. The introduction section was extended to state the position of current study in the field.
4. The structure of the paper was already provided at the end of the introduction section.
5. The flowchart of the CBV algorithm was added.
6. The statistical tests were performed, and their results were added in the tables.

Round 2

Reviewer 1 Report

The manuscript in its revised form has been considerably improved and now it can be published.

Reviewer 2 Report

Authors have completely addressed all my concerns.