Gaussian Naïve Bayes Algorithm: A Reliable Technique Involved in the Assortment of the Segregation in Cancer

Algorithm 1

Gaussian Naïve Bayes.

	Input: dataset
	S//dataset attributes involved
	Output: class name
	Start
(1)	To train the date splitting the dataset into 70%, the remaining 30% is used for testing.
(2)	Training phase
(2.1)
	is class in the dataset of training.
(2.2)	Probability of every single class is calculating
	= frequency ()/total
(2.3)	Calculate the mean () as well as the standard deviation () values of each of the training dataset class attributes. Note down the result.
(3)	Testing phase
(3.1)	In testing DSX is an instance.
(3.2)	By applying equation (1), the probability density function (PDF) value of X is calculated at for values of X attributes remain in S, p ().
(3.3)	By using the equation, P (X| ) =  P (), for the values, resulting from step of 3.2, the conditional probability value of X is calculated at ,
(3.4)	By using an equation, , here, p () represents the probability value of instance at , and then, the posterior probability of X can be calculated.
(3.5)	By selecting maximization , assign a X class label.
(4)	Return the class name
	End of the process