[mlpack] GSoc 2019 - mlpack : evolutionary algortihm project proposal idea discussion

[Marcus Edel]

Hello Naman,

thanks for the clarification. I like the idea and I think this could fit well
into the mlpack codebase. One point that you should focus on in your proposal is
how this could be implemented so that it integrates well into mlpack; what
methods can be reused what has to be added.

Let me know if I should clarify anything.

Thanks,
Marcus

> On 19. Mar 2019, at 20:58, Naman Gupta <namangupta0227 at gmail.com> wrote:
> 
> Hi Marcus,
> 
> First of all, thank you for your quick response. In my previous email, I mentioned all the algorithms I have implemented in my research work. They all perform well as feature selection methods (filter and wrapper both) when compared to the traditional Grid search and Random Search algorithms. As per your suggestion, I would like to point out that out of them, Grey wolf algorithm, Crow Search and Whale Optimization algorithm perform significantly well on a number of datasets. I have tested their performance on a number of datasets as follows:
> 1. Parkinson's Dataset
>      a) Audio dataset 
>      b) Image dataset
>      c) Text dataset
> 2. Thyroid dataset
> 3. Medical images
>      a) CT scan images
>      b) Microscopic images
>      c) MRI scans
> 4. Protein structure dataset
> 5. White Blood Cells images dataset
> 
> *All these datasets have high dimensionality. Some of them have both small instance set + high dimensionality which is a great challenge in machine learning and has not been addressed effectively.
> 
> According to a comprehensive survey by my lab and the aforementioned implementations, these 3 algorithms are most suitable for feature selection methods. In the last few years, bio-inspired optimization algorithms are recognized in machine learning to address the optimal solutions for complex problems in science and engineering. One of such problem: "High dimensionality problem"  which in turn requires high computational time and affects the classification accuracy due to noisy features, redundant etc. Feature selection reduces the dimensionality of the data by eliminating features which are noisy, redundant, and irrelevant for a classification problem. It is most often a challenge for the researchers due to its computational complexity.
> So, I would like to model and implement these 3 algorithms as feature selection methods (filter based and wrapper-based both) in mlpack this year. The users will be able to modify the algorithm and will be able to define their own fitness functions and many more features.
> 
> Please let me know what you think.
> 
> Thank you for your time and consideration.
> 
> Naman Gupta
> 
> 
> 
> On Tue, Mar 19, 2019 at 2:59 AM Marcus Edel <marcus.edel at fu-berlin.de <mailto:marcus.edel at fu-berlin.de>> wrote:
> Hello Naman,
> 
> welcome and thanks for getting in touch. I like the overall idea, but I'm not
> entirely sure which method you propose to implement; it sounds like you like to
> work on: Crow search algorithm, Grey wolf optimizer, Cuttlefish algorithm, Whale
> Optimization algorithm, Ant lion optimizer, personally I would focus on one or
> two methods that have a record to perform well on certain tasks. Let me know
> what you think.
> 
> Thanks,
> Marcus
> 
>> On 18. Mar 2019, at 16:30, Naman Gupta <namangupta0227 at gmail.com <mailto:namangupta0227 at gmail.com>> wrote:
>> 
>> Hello Everyone. 
>> 
>> I am Naman Gupta, a computer science undergraduate student at MAIT, GGSIPU, Delhi, India. I have been working on bio-inspired evolutionary algorithms for the past 2 years and I have developed and implemented various optimized versions of different bio-inspired algorithms in various fields including Ad hoc networks, Medical Image Processing, and NLP. Some of my work has been published in SCI-indexed journals (Q1 ranking).
>> 
>> I have been working on bio-inspired algorithms namely, Crow search algorithm, Grey wolf optimizer, Cuttlefish algorithm, Whale Optimization algorithm, Ant lion optimizer, etc. and their usability in various domains. These algorithms are inspired by the social behavior of animals in nature and provide far more superior results when compared to the state of the algorithms (Evolutionary and Genetic algorithms). Bio-inspired algorithms are gaining popularity day by day because of their capability of finding solutions to NP-hard problems and are being applied to a myriad of optimization engineering problems like Thermal design, Structural optimization, Satellite layout design etc. I have the statistics of over ten years representing the growing number of applications of these algorithms. I have developed and modeled these algorithms as feature selection algorithms (filter based and wrapper based) which finds the most optimal feature subset from a large feature dataset. It resolves the “curse of dimensionality” problem more efficiently and with less computational time and higher classification accuracy. I have already implemented the aforementioned algorithms in python during my research work.
>> 
>> I am very much interested in contributing to mlpack in GSoC'19. Now, I want to implement these algorithms in mlpack as feature selection mathods. These algorithms are population-based, Meta-heuristic optimization techniques and are simple, flexible, and avoids local optima. They search the global search space in less computation time as compared to the traditional approaches Grid search and Random Search. They will enhance the classification accuracy and will reduce the computational time.
>> 
>> It would be a great help if the mentors could provide me some insight into this proposal idea. Can I propose this idea? Can you please suggest me something to make it better. I will add more details, more functionality, and features in the final proposal, this is just an abstract. I look forward to hearing from you.
>> 
>> 
>> 
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