Arul Bharathi

The need of machine learning in the defence planning and strategies is increasing day by day due to the increasing amount of breaches and decimations caused by terrorist forces. A myriad of military bases, temporary campaigns, base camps etc. are being targeted and attacked by several terrorist forces. The common problem in the warfare and tumultuous international borders is the frequent and violent intrusion and breaches upon the temporary / permanent military and army bases. Though they are successful in their individual task to identify the safest or the effective base, a combined location that embraces both effectiveness and vulnerability is invalid using a present analyzing and classification technology. This problem is due to the presence of collinearity between the parameters that determine both effectiveness and vulnerability. A military base location can be both effective and vulnerable at the same time, a location that does not provide sufficient effectiveness to perform military operation. To combat this problem, in this paper we propose an algorithm that identifies the two rival parameters (effectiveness and vulnerability) and cross correlates them one by one for checking collinearity between them. Additionally, after identifying the collinear combinations, the Rival Check Cross Correlation Algorithm eliminates those collinear combinations, thereby providing unambiguous combinations of effective variables.