In [Ji and Carin, 2007, they define the problem of costsensitive classification and use a partially observable Markov decision process to tradeoff the cost of acquiring additional measurements We propose a reinforcement learning based approach to tackle the costsensitive learning problem where each input feature has a specific cost.
The acquisition process is handled through a stochastic policy which allows features to be acquired in an adaptive way. The general architecture of our From each training case, we randomly generate p 20 feature acquisition processes (by permuting the order in which the features were acquired; see Appendix A) and obtain totally 512 20 features acquisition processes to train a costsensitive classifier.
Sequential CostSensitive Feature Acquisition 285 the patient and his previous results on other exams. Moreover, it may happen that the acquisition cost varies from a feature to another, as in medical diagnosis again, where some medical results are cheap to acquire (e. g. blood analysis), We formally define the costsensitive classification problem and solve it via a partially observable Markov decision process (POMDP). While the POMDP constitutes an intuitively appealing formulation, the intrinsic properties of classification tasks resist application of it to this problem.
A graphical representation of IOHMM modeling of a feature acquisition process for a given class, where s t; a t and o t denote the state, action and observation at the time step t, respectively. The applications stimulate new needs for costsensitive classification, such as using the costs to approximate the true evaluation criteria of interest (Ruan, 2013), or being both costsensitive and errorsensitive (Jan, 2012).
Merger And Acquisition Essay In the world of growing economy and globalization, major companies on both domestic and international markets struggle to achieve the optimum market share possible. Every day business people from top to lower management work to achieve a common goal being the best at what you do, and