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Many thanks for revising your manuscript After consulting experienced reviewers and our own assessment of the manuscript, I am happy to inform you that your paper is accepted.
[# PeerJ Staff Note - this decision was reviewed and approved by Jyotismita Chaki, a PeerJ Computer Science Section Editor covering this Section #]
The authors seems to have incorporated the comments, therefore, it can be conisidered
The authors seems to have incorporated the comments, therefore, it can be conisidered
The authors seems to have incorporated the comments, therefore, it can be conisidered
The authors seems to have incorporated the comments, therefore, it can be conisidered
The paper needs major improvements e.g. the comparison with state of the art and the other issues mentioned by the reviewers. Please address the concerns, questions, and suggestions of the reviewers according to basic reporting, validity of the findings, and validity of the findings. Thank you...
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
This manuscript introduces different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using multiobjective evolutionary algorithms, and provides an experimental comparison of lossless and low-loss schemes for text representation. The experiment results show the effectiveness of the proposed algorithm.
(1) It should be clearer for show and introduce the reformulated multiobjective optimization problem. Please define the reformulated multiobjective optimization problem using the classic multiobjective optimization problem definition format, to make it more understandable and readable.
(2) Please add more details about the feature reduction.
(3) I wonder that the number of the original dimensions of the datasets and the number of dimensions after dimensionality reduction.
(4) The comparison in Figure 2 may be pointless. Please display the results in another form.
(5) The experimental results are not sufficient. Please add more convincing results.
This paper proposes a Multiobjective Evolutionary Optimization for dimensionality reduction of texts represented by synsets, I have a few concerns below
The abstract needs to be further improved to show the clear contribution of the proposed work along with its validation/justification by comparing it with state-of-the-art.
There should be a clear problem statement and the objectives, the author achieved in the study.
the data set, its volume, the sources of collection of the data set and the preprocessing applied by the authors could be moved at the start of the experimental protocol. The data pre-processing should be discussed in detail. E.g what were the data attribute, how did you tokenize it and what noises were removed?
What are the basis to get Table1 ?, eg. How the values has been calculated ?
The main deficiency of this work is the missing evaluation, the author should give a comparison of their work with state-of-the-art works to validate their work. It is suggested to apply the proposed technique to some other text spam detectors so that the variety of the text and work could be better understood. The one suggested could be the Gmail spam detector or any other email spam detector which generally detects the spam in the text data
This paper proposes a Multiobjective Evolutionary Optimization for dimensionality reduction of texts represented by synsets, I have a few concerns below
The abstract needs to be further improved to show the clear contribution of the proposed work along with its validation/justification by comparing it with state-of-the-art.
There should be a clear problem statement and the objectives, the author achieved in the study.
the data set, its volume, the sources of collection of the data set and the preprocessing applied by the authors could be moved at the start of the experimental protocol. The data pre-processing should be discussed in detail. E.g what were the data attribute, how did you tokenize it and what noises were removed?
What are the basis to get Table1 ?, eg. How the values has been calculated ?
The main deficiency of this work is the missing evaluation, the author should give a comparison of their work with state-of-the-art works to validate their work. It is suggested to apply the proposed technique to some other text spam detectors so that the variety of the text and work could be better understood. The one suggested could be the Gmail spam detector or any other email spam detector which generally detects the spam in the text data
This paper proposes a Multiobjective Evolutionary Optimization for dimensionality reduction of texts represented by synsets, I have a few concerns below
The abstract needs to be further improved to show the clear contribution of the proposed work along with its validation/justification by comparing it with state-of-the-art.
There should be a clear problem statement and the objectives, the author achieved in the study.
the data set, its volume, the sources of collection of the data set and the preprocessing applied by the authors could be moved at the start of the experimental protocol. The data pre-processing should be discussed in detail. E.g what were the data attribute, how did you tokenize it and what noises were removed?
What are the basis to get Table1 ?, eg. How the values has been calculated ?
The main deficiency of this work is the missing evaluation, the author should give a comparison of their work with state-of-the-art works to validate their work. It is suggested to apply the proposed technique to some other text spam detectors so that the variety of the text and work could be better understood. The one suggested could be the Gmail spam detector or any other email spam detector which generally detects the spam in the text data
This paper proposes a Multiobjective Evolutionary Optimization for dimensionality reduction of texts represented by synsets, I have a few concerns below
The abstract needs to be further improved to show the clear contribution of the proposed work along with its validation/justification by comparing it with state-of-the-art.
There should be a clear problem statement and the objectives, the author achieved in the study.
the data set, its volume, the sources of collection of the data set and the preprocessing applied by the authors could be moved at the start of the experimental protocol. The data pre-processing should be discussed in detail. E.g what were the data attribute, how did you tokenize it and what noises were removed?
What are the basis to get Table1 ?, eg. How the values has been calculated ?
The main deficiency of this work is the missing evaluation, the author should give a comparison of their work with state-of-the-art works to validate their work. It is suggested to apply the proposed technique to some other text spam detectors so that the variety of the text and work could be better understood. The one suggested could be the Gmail spam detector or any other email spam detector which generally detects the spam in the text data
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