Feature Selection With Genetic Algorithms - Code and Plots 

watch this space and I shall upload more videos about extensions of GA.

Hi Pravin. I am glad to help. At some stage I play to move from feature selection to automatic feature engineering. In my experience it boosts the performance many times. If you are subscribed, you will get notified.

@@enlightenment609 Oh that's cool. Looking forward for that. I have subscribed you. You have a lot of useful content.

Excellent explanation. But finally you didn't specify the selected features from given data

@@ushanandhini1942 you can extract the best model at the end. See the description beneath the video to find the code that can help you do that.

Happy to help. You can get the fittest model using the code here: www.dropbox.com/s/uxqr2dmioa30c53/getBestLmModel.R?dl=0

The full script is provided in the links below the the video. Download it and follow the instructions.

@@enlightenment609 Thanks I was able to understand the entire process and successful run the code. The only thing is my code tend to generate the same iteration result in each run.

@@tusuubiraedward9600 There can be many reasons. If the problem is too easy, population size is too small, or the random seed is the same then you will get the same answer. For the encoded problem, it is likely to be too easy: Picking from 4/5 attributes is not hard for a large enough population. Try some other problem where number of features is larger.

Your comment does not clarify why and how do you want to modify the chromosome just to predict missing values (from what?). ) If you want to build a classifier that predicts if a dataset/record has missing values then GA must only select features for such a classifier. In this case the same binary chromosome applies.

if you have already selected 5 features using genetic algorithm, then you must already have written a fitness function. Did you use a classification algorithm with the GA?

@@enlightenment609 logistic regression

Yes, so just like the video uses linear Regression inside the fitness function, you can I stead call logistic Regression inside it. In fact the drop box code gives you a file called class feature fitness.R that uses classification with GA. Only it uses decision tree instead of logistic Regression.

nvm I just found it

Hi Luis. The code provided in the description beneath the video also contains the code to access the dataset. It is all self contained. Let me know if you didn't follow that.

Yes, that's very easy. You set the chromosome length = 23 in runGA.R. Then set the fitness function up to use the right dataset as well. See examples in class-feature-fitness.R.

@@enlightenment609 thanks a lot

You mean feature engineering with Genetic Programming? Yes, I have it in the plans. And in my experience it works very well. I have done it with Grammatical Evolution and it is a lot easier to do than with Genetic Programming.

You can search for GA toolkits in Python. You must then write a fitness function similar to what you have seen here and attach it to the Python code. For example, you can search for DEAP toolkit.

@@enlightenment609 Thank you for these efforts...me too I need a similar code with python can you help me...

if you currently have a code, please share it with me