Feature Generation Algorithm: Difference between revisions

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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 feature]]s).
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 feature]]s).
* <B>AKA:</B> [[Feature Creation Algorithm|Feature Extraction Algorithm]].
* <B>Context:</B>
* <B>Context:</B>
** It can support [[ML Predictive Quality Improvement]]s and [[Data Preprocessing Processing Improvement]]s.
** It can support [[ML Predictive Quality Improvement]]s and [[Data Preprocessing Processing Improvement]]s.
** It can involve techniques such as [[Feature Extraction]], [[Feature Selection]], and [[Dimensionality Reduction]].
** 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 be critical in domains where raw data is complex and high-dimensional.
** It can use domain knowledge to create features that are more relevant to specific [[Machine Learning Task]]s.
** It can use domain knowledge to create more relevant features to specific [[Machine Learning Task]]s.
** 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]].
** ...
** ...
* <B>Example(s):</B>  
* <B>Example(s):</B>  
** In [[text mining]], a feature generation algorithm might extract [[n-gram]]s or [[sentiment scores]] from raw text.
** A [[Text Classification Feature Generation Algorithm]], that extracts [[n-gram]]s or [[sentiment scores]].
** In [[finance]], it might generate features like moving averages or volatility measures from stock price data.
** 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.
** In [[image recognition]], algorithms could generate features by identifying edges, textures, or color histograms in images.
** ...
** ...
* <B>Counter-Example(s):</B>  
* <B>Counter-Example(s):</B>  
** [[Feature Normalization Algorithm]]
** a [[Feature Normalization Algorithm]].
** [[Data Cleaning Algorithm]]
** a [[Data Cleaning Algorithm]].
* <B>See:</B> [[Feature Engineering]], [[Machine Learning Pipeline]], [[Data Preprocessing]].
** a [[Feature Dimensionality-Reduction Algorithm]], such as a [[feature selection algorithm]].
* <B>See:</B> [[Feature Engineering]], [[Machine Learning Pipeline]], [[Data Preprocessing]], [[Feature Weighting Algorithm]], [[Text Item Feature]].


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== References ==
=== 2003 ===
* ([[2003_FeatureExtractionbyNonParametri|Torkkola, 2003]]) ⇒ [[Kari Torkkola]]. ([[2003]]). “[http://machinelearning.wustl.edu/mlpapers/paper_files/Torkkola03.pdf 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 label]]s and [[transformed feature]]s. </s> 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 assumption]]s about [[class densiti]]es. </s>
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[[Category:Concept]]
[[Category:Concept]]
[[Category:Machine Learning]]
[[Category:Machine Learning]]
[[Category:Quality Silver]]
[[Category:Quality Silver]]

Latest revision as of 08:25, 15 January 2024

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).



References

2003