The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.
Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.
We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on Workshop sessions, 3 and 6 hour duration.
Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.
We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.
We will notify you about the status of your proposal within three weeks of submission.
Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
Commitment to open source
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
Key dates and deadlines
- Revised paper submission deadline: 17 June 2016
- Confirmed talks announcement (in batches): 13 June 2016
- Schedule announcement: 30 June 2016
- Conference dates: 28-29 July 2016
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Building a Large scale Augmented classifier ensemble to predict in noisy data
Different types of classifiers were investigated in the context of classification of problem tickets in the Enterprise domain. There were still challenges in building an accurate classifier post data cleaning and other accuracy improving pre-processing techniques. Creating an ensemble of classifiers gave better accuracy than individual classifiers. The maximum accuracy was got by enhancing the ensemble with an additional automatically generated domain specific class wise patterns.
Use of this system gave us greater than 4 percent improvement over the techniques of just using the ensemble classifier.
Takeaways - Challenges in Real life classification, Data curation techniques, Ensemble classification, Increasing the accuracy of ensemble through proposed techniques, Building a scalable prediction system
Employees face a lot of issues across different functions in the organization like Payroll, Infrastructure, Facilities, Applications, etc. When employees raise a ticket they are traditionally asked to manually select a category for their ticket from a hierarchical menu driven interface. This leads to a lot of wrong selection of choice by end user. This in turn causes tickets to be raised in wrong bucket and delays the resolutions due to re-assignments.
We have built a system which accepts a Natural language text input from end user and automatically classifies the ticket in the right category. Key challenges in building such a system are the inaccurate training corpus, many closely separated classes, imbalanced classes, many classes with too less data and a need to handle a large number of requests.
The challenges faced and technical steps needed to build such a system are described. Presentation will take you through the evolution of the system solution, Limitations of solution at each stage, Accuracy achieved across different stage, and WHat improvement in solution architecture is added at each of the stages.
Pre-requisite knowledge of machine learning and classification will be helpful
Arthi Venkataraman has 19+ years of experience in the design, development and testing of projects in different domains • She is currently a Senior Member in the Distinguished Members of Technical Staff cadre at Wipro Technologies • Her current role involves solution development for different business problems in the technology area of Natural Language Processing, Machine Learning and Semantics Technologies
She has a B.E Degree in Computer Science from University Visvesvariah College of Engineering, Bangalore and an MBA (PGDSM) from IIM, Bangalore. She has previously presented papers and spoken at other international conferences This presentation is based on Arthi’s experience in area of building a large scale production grade classifier using Python at her organization
- Conferences Spoken At.
- Spoke at Grace Hopper 2015 - https://www.youtube.com/channel/UCGuAMvPJ0l9xscCfoxUpEDA
- Spoke at Pycon 2015 - (https://in.pycon.org/2015/ )
- Spoke at Fifth Elephant 2013 https://fifthelephant.talkfunnel.com/2013/644-similar-entity-detection-in-large-data
- Papers Published.