Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 122-130.doi: 10.6040/j.issn.1672-3961.0.2018.211

• Machine Learning & Data Mining • Previous Articles    

A rule extraction method based on multi-objective co-evolutionarygenetic algorithm

Zhongwei ZHANG(),Hongyan MEI*(),Jun ZHOU,Huiping JIA   

  1. School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou 121000, Liaoning, China
  • Received:2018-05-25 Online:2019-04-20 Published:2019-04-19
  • Contact: Hongyan MEI E-mail:zzwzzwcool@126.com;liaoning_mhy@126.com
  • Supported by:
    辽宁省科学技术计划项目面上项目(2015020089);辽宁省科学技术计划项目面上项目(201602372)

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Abstract:

Aiming to deal with the problem that continuous numerical attributes in transaction database were difficult to divide and the efficiency of rule extraction was low, a multi-objective co-evolutionary quantification association rule extraction method was proposed with the cooperative of crossover population and mutation population. The non-dominated sorting of the Pareto principle was used to optimize the individuals of population. Genotype and phenotype of individuals similarity were used to control the matching of individuals in the crossover population. The mutation population was segmented by the concept of level set, then, single point mutation and multiple point mutation were adopted according to the quality of individuals to enhance the individuals diversity. The pareto optimal solution set was obtained from the elite population which was used to preserve the excellent individuals in the crossover population and the mutation population. The simulation results on different datasets showed that the algorithm achieved a good balance of performance and quantity, and the data set was effectively covered, which verifies the effectiveness and feasibility of the algorithm.

Key words: multi-objective, co-evolutionary, non-dominated, similarity, level set

CLC Number: 

  • TP311.13

Table 1

The encoding of chromosomes"

A1 A2 Ai AN
T1 LB1 UB1 T2 LB2 UB2 Ti LBi UBi TN LBN UBN

Fig.1

The evolution model of crossover population"

Fig.2

The evolution model of mutation population"

Fig.3

The model of algorithm"

Table 2

The evolutionary parameter of genetic algorithm"

种群大小 PCx PCn Pm 迭代次数
50 0.99 0.5 0.2 200

Table 3

The description of some datasets"

数据集 记录个数 属性个数 目标属性
Basketball 96 5 Points per minute
Bodyfat 252 18 Body height
Quake 2 178 4 Richter

Table 4

Some rules extracted from the Basketball dataset"

规则序号 规则前件 规则后件 置信度 可理解度 兴趣度
1 assists_per_minute∈(0.177 4, 0.337 8) height∈(159.011 7, 193.104 2)
points_per_minute∈(0.087 3, 0.773 9)		 1 0.792 5 0.302 3
2 assists_per_minute∈(0.148 1, 0.291 6) height∈(154.136 8, 188.847 5)
time_played∈(5.578 0, 43.171 3)
age∈(22.613 6, 36.458 2)
points_per_minute∈(0.214 7, 0.691 7)		 0.641 5 0.898 2 0.352 2
3 Height∈(169.988 1, 185.075 1) assists_per_minute∈(0.154 6, 0.359 5)
age∈(22.613 6, 36.458 2)
points_per_minute∈(0.214 7, 0.691 7)		 0.931 0 0.861 4 0.354 3

Fig.4

The mean value of confidence varies with the number of iterations"

Fig.5

The mean value of comprehensibility varies with the number of iterations"

Fig.6

The mean value of interestingness varies with the number of iterations"

Table 5

The mean value of support and size of extracted rules"

数据集 支持度/% 规则大小
MODENAR RPSO GAR MOGAR REM_MOCGA MODENAR RPSO GAR MOGAR REM_MOCGA
Basketball 37.20 36.44 36.69 50.82 46.93 3.21 3.21 3.38 3.24 3.83
Bodyfat 65.22 65.22 65.26 57.22 41.17 6.87 7.06 7.45 6.96 7.90
Quake 39.86 38.74 38.65 30.12 47.69 2.03 2.22 2.33 2.38 3.12

Table 6

The number and the mean value of confidence about extracted rules"

数据集 提取规则数量 置信度/%
MODENAR RPSO MOGAR REM_MOCGA MODENAR RPSO MOGAR REM_MOCGA
Basketball 48.0 34.2 50 152.3 61±2.1 60±2.8 83 88±2.9
Bodyfat 52.4 46.4 84 311.4 62±3.2 61±1.8 85 85±2.3
Quake 55.4 46.4 44.87 332.6 63±2.8 63±2.8 82 92±3.4

Table 7

The mean value of coverage about extracted rules"

%
数据集 MODENAR RPSO GAR MOGAR REM_MOCGA
Basketball 100.00 100.00 100.00 100.00 100.00
Bodyfat 86.11 86.11 86.00 93.52 97.22
Quake 88.9 87.92 87.5 91.07 99.68
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