Feature Generation Algorithm
(Redirected from featurization algorithm)
Jump to navigation
Jump to search
A Feature Generation Algorithm is a data processing algorithm that can be implemented by a feature generation system to solve feature generation task (sot create new ML features).
- AKA: Feature Extraction Algorithm.
- Context:
- It can support ML Predictive Quality Improvements and Data Preprocessing Processing Improvements.
- It can involve techniques such as Feature Extraction, Feature Selection, and Dimensionality Reduction.
- It can be critical in domains where raw data is complex and high-dimensional.
- It can use domain knowledge to create more relevant features to specific Machine Learning Tasks.
- It can range from being a Heuristic Feature Creation Algorithm to being a Data-Driven Feature Creation Algorithm.
- It can range from being a Low-Level Feature Creation Algorithm to being a High-Level Feature Creation Algorithm.
- ...
- Example(s):
- A Text Classification Feature Generation Algorithm, that extracts n-grams or sentiment scores.
- In finance, it might generate features like moving averages or volatility measures from stock price data.
- In image recognition, algorithms could generate features by identifying edges, textures, or color histograms in images.
- ...
- Counter-Example(s):
- See: Feature Engineering, Machine Learning Pipeline, Data Preprocessing, Feature Weighting Algorithm, Text Item Feature.
References
2003
- (Torkkola, 2003) ⇒ Kari Torkkola. (2003). “Feature Extraction by Non Parametric Mutual Information Maximization.” In: The Journal of Machine Learning Research, 3.
- QUOTE: We present a method for learning discriminative feature transforms using as criterion the mutual information between class labels and transformed features. Instead of a commonly used mutual information measure based on Kullback-Leibler divergence, we use a quadratic divergence measure, which allows us to make an efficient non-parametric implementation and requires no prior assumptions about class densities.