Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications. Contact: yvan. During the last decade, the motivation for applying feature selection FS techniques in bioinformatics has shifted from being an illustrative example to becoming a real prerequisite for model building.
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PhD Program : Electrical and Computer Engineering
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A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model the probability distribution of different observations given the underlying state and the underlying MDP. Unlike the policy function in MDP which maps the underlying states to the actions, POMDP's policy is a mapping from the observations or belief states to the actions. The POMDP framework is general enough to model a variety of real-world sequential decision processes.