Design of Cost Sensitive Classifiers for E-Learning Data Sets Tuning with Cost Ratio

 
 
 
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  • Abstract


    An examination on costly classifiers impacting essential choices utilizing expectations is a vital research field for the information mining analysts. Notwithstanding, the determination of parameters for such classifiers assumes an imperative job in getting more exactness and less expense in the basic setting. Following this rule, a measurement dependent on a levelheaded number, dictated by the proportion between the quantities of false positives to false negatives in assessing the classifiers is considered. Cost delicate models too the cost dazzle models are normally both acknowledged by their execution through least blunder or most extreme exactness. Thus in setting of understudies points of interest from East London locale and from Yorkshire district should be connected with more significant measures to locate the correct minimal effort esteems. In this paper, we analyze the cost touchy classifiers and measure their execution by shifting the parameter (False Positive and False Negative). We recognize distinctive examples of conduct of these classifiers for various scope of qualities. Add up to cost for four unique reaches are investigated independently and the exhibitions in the two-distinctive setting of understudy detail from East London district and from Yorkshire locale are considered. Add up to cost of understudy subtle elements from East London area happens to be more than expense of Yorkshire district while tuning the parameters. These discoveries can bolster the choices of diagnosing which methods for instruction is more important to choose with foruming or non-foruming with more certainty.

     

     


  • Keywords


    cost sensitive classification, learning, data mining, prediction

  • References


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Article ID: 27741
 
DOI: 10.14419/ijet.v7i4.39.27741




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