• B Premamayudu
• P Subbarao

Rule Induction in the space of AI characterizes formal guidelines that are acquired from a bunch of perceptions. MODLEM is a utilization of the standard enlistment calculation. In this paper, to improve the exhibition of this characterization, a group with boosting approach is viewed as where tests are picked by their likelihood dispersion that updates relatively to the example mistake. For this model, distinctive informational indexes have been gathered from UCI AI archive site. In light of the gathered information, the best dataset is picked and furthermore contrasted and existing models like Naïve Bayes, RBF, MLP, OneR and accomplishes better exactness.

Classification, Rule, Boosting, MODLEM, Data mining

"Classification of Rules Induced by MODLEM via Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.f626-f632, May-2021, Available :http://www.jetir.org/papers/JETIR2105748.pdf

"Classification of Rules Induced by MODLEM via Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppf626-f632, May-2021, Available at : http://www.jetir.org/papers/JETIR2105748.pdf