About the 2019 edition:

The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule

The conference has three tracks:

Talks in the main conference hall track
Poster sessions featuring novel ideas and projects in the poster session track
Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.

Who should attend Anthill Inside:

Anthill Inside is a platform for:

Data scientists
AI, DL and ML engineers
Cloud providers
Companies which make tooling for AI, ML and Deep Learning
Companies working with NLP and Computer Vision who want to share their work and learnings with the community

For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com

Sponsors:

Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.

Anthill Inside 2019 sponsors:

Bronze Sponsor

Community Sponsor

Hosted by

Anthill Inside

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

All submissions

Previous Next

Feature selection and engineering using genetic algorithms and genetic programming

Submitted Apr 30, 2019

Section: Full talk Technical level: Advanced Session type: Lecture

While feature selection is almost a solved problem in data science, feature engineering is still quite a mystery. In this talk I will outline a method that I use to solve feature engineering, with a goal to provide a generalized framework to tackle both feature engineering and selection simultaneoously.

Outline

The first few slides will talk about the application of genetic algorithms (GA) to feature selection. The next couple of slides will talk about advancements made to GAs by use of a multi-dimensional covariance map, a method that I developed. The next couple of slides will talk about genetic programming (GP) and how one can use the multi-dimensional covariance map to augment the convergence of GPs.