We love features in our data, lots of features, in the same way that we love features in our toys, mobile phones, cars, and other gadgets. Good features in our big data collection empower us to build accurate predictive models, identify the most informative trends in our data, discover insightful patterns, and select the most descriptive parameters for data visualizations. Therefore, it is no surprise that feature mining is one aspect of data science that appeals to all data scientists. Feature mining includes: (1) feature generation (from combinations of existing attributes), (2) feature selection (for mining and knowledge discovery), and (3) feature extraction (for operational systems, decision support, and reuse in various analytics processes, dashboards, and pipelines).
Learn more about feature mining and feature selection for Big Data Analytics in these publications:
- Feature-Rich Toys and Data
- Interactive Visualization-enabled Feature Selection and Model Creation
- Feature Selection (available on the National Science Bowl blog site)
- Feature Selection Methods used with different Data Mining algorithms
- (and for heavy data science pundits) Computational Methods of Feature Selection
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