Mining Tourist Motive for Marketing Development via Twice-Learning

Figures & data

FIGURE 1 The twice-learning framework.

TABLE 1 The Pseudocode of C4.5 Rule-PANE Algorithm (Zhou and Jiang 2003)

Algorithm: C4.5 Rule-PANE
Input: training set S, the number of additional training instance m
Process:
N* = Bagging (S)    /* generate neural network ensemble using Bagging */
S’ = Φ; S’’= Φ
for i = 1 to n  {    /* process the training set by the trained ensemble */
yi’ = N*(xi: (xi, yi) ∈ S)
S’ = S’ ∪ {(xi, yi’)}
}
for i = 1 to m  {    /* generate additional training data set using ensemble*/
xj = RandVec()   /* generate a random feature vector */
yj’ = N*(xj)
S’’ = S’’ ∪ {(xj, yj’)}
}
SR = S’ ∪ S’’      /* construct the training data set for rule learning */
R = Rule Inducer(SR)
Output: rule set R

TABLE 2 The Detail Information of the Attributes

ID	Att_Name	Possible Values
1	Gender	Male/Female
2	Age	18–24/25–34/35–44/45–54/55–65
3	Education	High School or below/Junior College/University/Postgraduate
4	Occupation	Students/Teachers/White-collar workers/Blue-collar workers/Managers or Executives/Retirees/Others
5	Personal monthly income (RMB)	3000 or below/3001–5000/5001–10000/10001or above
6	Life-cycle stage	Single/Married, with no children/Married, with children/Others
7	Being well-respected	1(not important at all)˜5(very important)
8	Fun and enjoyment in life	1(not important at all)˜5(very important)
9	Security	1(not important at all)˜5(very important)
10	A sense of belonging	1(not important at all)˜5(very important)
11	Excitement	1(not important at all)˜5(very important)
12	Self-fulfillment	1(not important at all)˜5(very important)
13	Self-determination	1(not important at all)˜5(very important)
14	Convenience	1(not important at all)˜5(very important)

TABLE 3 p-Values of the Pairwise t-Test between Any Two Compared Methods (Significance Level 95%)

Methods	Naïve Bayes	C4.5 Rule	Neural Network	Neural Network Ensemble	C4.5 Rule-PANE
Naïve Bayes	—	—	—	—	—
C4.5 Rule	1.12E-02	—	—	—	—
Neural Network	6.86E-04	1.45E-07	—	—	—
Neural Network Ensemble	4.52E-04	5.07E-08	3.97E-01	—	—
C4.5 Rule-PANE	9.62E-06	1.36E-10	6.80E-02	1.99E-01	—

FIGURE 2 Error rates of all the compared methods in analyzing the current dataset.

FIGURE 3 Gain ratios of the attributes obtained from C4.5 Rule-PANE.

FIGURE 4 Gain ratios of the attributes obtained from C4.5 Rule.

TABLE 4 Decision Rules Learned from C4.5 Rule-PANE

Rule ID	Precondition	Decision Result
R1	IF Age = 35–44 AND Life-cycle stage = Married, with children AND Occupation = White-collar worker AND Being well-respected ≤ Average	physical
R2	IF Age = 25–34 AND Life-cycle stage = Married, with no children AND Education ≥ University AND Security ≥ Average	physical
R3	IF Gender = Female AND Education = High school or below AND Excitement ≤ Unimportant AND Convenience ≥ Important	physical
R4	IF Life-cycle stage = Single AND Education = University AND Self-determination ≥ Important AND Self-fulfillment = Very Important AND Security ≥ Important	cultural
R5	IF Age = 18–24 AND Occupation = Student AND Self-fulfillment ≥ important AND Fun and enjoyment in life ≤ Average	cultural
R6	IF Gender = Male, Income = 10001 or above, AND Excitement ≤ Unimportant AND Self-fulfillment ≥ Important	cultural

Zhou, Z.-H., and Y. Jiang. 2003. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine 7(1):37–42.

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