Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

Algorithm 4

Ensemble classification.

	Data: Training Set
	BC: Number of base classifiers
	SR: Ratio of samples that need replacement
	: Parameter used to reduce the distance between training and synthetic data
	: i^th attribute
	: standard deviation
	r: normal distribution’s sampling value N(0, 1)
	Training Phase:
	For a = 1: BC
	Copy the original dataset i.e. Data
	Identify the number of training samples that need replacement, i.e.,
	For b = 1: TS
	Randomly pick “z” samples from
	If x is a majority class sample, then
	Generate a neighborhood of z based on and replace z exists in
	Else if
	Check z is a minority sample, then compute m = Round
	Replace m neighbourhoods of z in
	End For
	Build base classifier from
	End For
	Classification Phase:
	For a given z
	evaluate ensemble to classify the sample z based on the majority voting strategy