Feature (machine learning)
In machine learning and pattern recognition, a feature is an individual measurable heuristic property of a phenomenon being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition.
The set of features of a given data instance is often grouped into a feature vector. The reason for doing this is that the vector can be treated mathematically. For example, many algorithms compute a score for classifying an instance into a particular category by linearly combining a feature vector with a vector of weights, using a linear predictor function.
While different areas of pattern recognition obviously have different features, once the features are decided, they are classified by a much smaller set of algorithms. These include nearest neighbor classification in multiple dimensions, neural networks or statistical techniques such as Bayesian approaches.
In character recognition, features may include horizontal and vertical profiles, number of internal holes, stroke detection and many others.
In spam detection algorithms, features may include whether certain email headers are present or absent, whether they are well formed, what language the email appears to be, the grammatical correctness of the text, Markovian frequency analysis and many others.
In all these cases, and many others, extracting features that are measurable by a computer is an art, and with the exception of some neural networking and genetic techniques that automatically intuit "features", hand selection of good features forms the basis of almost all classification algorithms.
|This article does not cite any references or sources. (August 2009)|