Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B

Algorithm 1

The description of CFS-GA algorithm.

CFS-GA:

Input:

Encoding records of the dataset with binary code;

Selection operator;

Crossover rate 𝑃_𝑐;

Mutation rate 𝑃_𝑚;

The iteration number of population 𝑔;

The initial amount of population 𝑃;

Output: Attributes selected by GA-CFS;

The description of GA-CFS:

(1) Initialize the population 𝑃, and generate 𝑃 attribute subsets randomly;

(2) To evaluate the population 𝑃 and calculate the Fitness value of each individual ℎ in the population;

(3) While (the optimal result not approached or less than iteration number)

 {

   (1) Selection operator, according to Fitness value, select the optimal individual

  from the parent generation to the next;

   (2) Crossover operator, according to Fitness value, select attribute subsets by

  from the parent generation, set the crossover point for each attribute subset, then

  swap the structures before or after the point for producing two new individuals;

   (3) Mutation operator, through the mutation rate and mutation operator,

   crossover subsets are mutated at random bits to produce two new individuals;

  (4) Add new individuals into the population to form a new one;

  (5) Evaluating individuals of the new population by Fitness value.

 }