Mastering feature selection: basics to developing your own algorithm
Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models
Outline/Structure of the Tutorial
- Importance of feature selection
- Discussion on state of the Art and some open Issues
- Some ideas to improve search based methods
- Clustering based feature selection techniques
- Analyzing and comparing correlation matrix of datasets
- A short discussion how this can be converted to a graph theory based formulation
- Participants will be able to articulate the need of feature selection
- will be able to articulate some of the open issues
- Understand how clustering based and analysis of data-set features can help this open issues
Prerequisites for Attendees
- Some knowledge of programming
- Basic idea abut machine learning
schedule Submitted 1 month ago
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