Creating Ensemble Classifiers with Information Entropy Diversity Measure

Algorithm 1

Incremental_SEM algorithm.

	Input:
Training dataset with labels by ensemble classifier Lt−f;
The interval threshold of classification diversity: [a, b];
Iterate number (each iterate creates a new base classifier: k)
Ensemble classifier at period of [t−f, t]: Lt−f;
Output: incremental ensemble classifier Lt at time t.
(1)	Begin
(2)	Loop
(3)	Compute diversity value λ0 of ensemble classifier Lt−f;
(4)	If
(5)	For i = 1 to k
(6)	Sampling training data from labeled dataset at period of [t−f, t] by Lt−f;
(7)	Generate a new base classifier Li;
(8)	Add Li to Lt−f;
(9)	Compute the diversity value λ1;
(10)	If
(11)	Lt = Lt−f ；
(12)	Return Lt
(13)	End for
(14)	else if
(15)	Compute the accuracy of each base classifier at Lt−f;
(16)	Sort base classifiers in decreasing order of accuracy as baselist;
(17)	Delete some member base classifiers with the lowest accuracy at Lt−f;
(18)	Update the Lt−f;
(19)	Lt = Lt−f;
(20)	return Lt;
(21)	else
(22)	Lt = Lt−f;
(23)	return Lt
(24)	End if
(25)	Break;
(26)	End loop