R/adas.r

In smotefamily: A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE

ADAS=function(X,target,K=5)   
{
	ncD=ncol(X) #The number of attributes
	n_target=table(target)
	classP=names(which.min(n_target))
	P_set=subset(X,target==names(which.min(n_target)))[sample(min(n_target)),]     #Extract a set of positive instances
	N_set=subset(X,target!=names(which.min(n_target)))
	P_class=rep(names(which.min(n_target)),nrow(P_set))
	N_class=target[target!=names(which.min(n_target))]
    sizeP=nrow(P_set) #The number of positive instances
	sizeN=nrow(N_set) #The number of negative instances
	Darr=rbind(P_set,N_set)  #The re-arranged data with positive first
	knear_D=knearest(Darr,P_set,K)
	knct=kncount(knear_D,sizeP)
	sum_of_negN=sum(knct[,2])
	knear_P=knearest(P_set,P_set,K) 
	num_syn_i=rep(0,sizeP)
	round((sizeN-sizeP)*knct[,2]/sum_of_negN)->num_syn_i
#print(nG)	
	syn_dat=NULL
	for(i in 1:sizeP)
		{    
			if(as.numeric(num_syn_i[i])>0)
				{  
				pair_idx = knear_P[i,ceiling(runif(num_syn_i[i])*K)]
				g = runif(num_syn_i[i])
				P_i = matrix(unlist(P_set[i,]),num_syn_i[i],ncD,byrow=TRUE)
				Q_i = as.matrix(P_set[pair_idx,])[,1:ncD]
				syn_i = P_i + g*(Q_i - P_i)
				syn_dat = rbind(syn_dat,syn_i)
				}
		}
	P_set[,ncD+1] = P_class		
	colnames(P_set)=c(colnames(X),"class")
	N_set[,ncD+1] = N_class
	colnames(N_set)=c(colnames(X),"class")
	rownames(syn_dat)= NULL
	syn_dat = data.frame(syn_dat)
	syn_dat[,ncD+1] = rep(names(which.min(n_target)),nrow(syn_dat))
	colnames(syn_dat)=c(colnames(X),"class")		
	NewD=rbind(P_set,syn_dat,N_set)
	rownames(NewD) = NULL


	D_result = list(data = NewD, syn_data = syn_dat, orig_N = N_set, orig_P = P_set, K = K, K_all = NULL, outcast = NULL, dup_size=num_syn_i,eps = NULL, method = "ADASYN")
	class(D_result) = "gen_data"	
	return(D_result)
}