Hello Marcus, Thank you for your positive response. I am working on the proposal now, and I will focus on the recommendations made by you. I will send you the proposal for review in 2-3 days. I am hoping to work with you under your guidance this summer as a part of GSoC 2019. Thank you for your time and consideration.
On Thu, Mar 21, 2019 at 3:46 AM Marcus Edel <marcus.e...@fu-berlin.de> wrote: > 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 <namangupta0...@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.e...@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 <namangupta0...@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. >> >> >> >> _______________________________________________ >> mlpack mailing list >> mlpack@lists.mlpack.org >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> >> >> >
_______________________________________________ mlpack mailing list mlpack@lists.mlpack.org http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack