Method for forecasting the volume of applications using probabilistic classifier based on Bayesian theorem for recruitment in the government organizations
Journal Title: International Journal on Computer Science and Engineering - Year 2012, Vol 4, Issue 12
Abstract
In this paper, we propose a practical method to estimate or forecast the volume of applications based on Bayesian classifier which is a probability method that makes optimal decision based on known probability distribution and recently observed data. By using the Bays estimate method, which selects spectrum analysis, the weight of forecasting model is obtained by carefully analyzing the sample space (various recruitments in the government organizations of Punjab, India). The sample calculation shows that the proposed method is highly reliable and improves the precision with a prior and likelihoods post probabilities of defined sample space. i.e. (i) development of an approximate distribution of likely volume of applications, (ii) prior probabilities of the volume of applications in various mutually exclusive events and (iii) likelihoods relating the confidence of forecast results for obtaining the Bayesian forecast of volume of applications. Earlier the forecasts are available, better will be their utility for government i.e. provides valuable information with regard to procedure fixation, improve service delivery, reduce costs, redefine administrative processes.
Authors and Affiliations
Sunil Kumar Chhillar , Baljit Singh Khehra
Keywords
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