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CHAPTER 6
TABLE 6.5
Before Shock After
GENETIC ALGORITHMS
Weights Before and After Mutation
W1A
W1B
W2A
W2B
W3A
W3B
W0A
W0B
W AZ
WB Z
W0Z
0.1 None 0.1
−0.4 −0.05 −0.45
0.7 None 0.7
−0.5 −0.07 −0.57
0.4 None 0.4
0.7 0.02 0.72
−0.1 None −0.1
0.1 None 0.1
−0.6 None −0.6
0.9 None 0.9
−0.3 None −0.3
The results in the Classifier output window show that naive Bayes achieves a very impressive 96.34% (658/683) classification accuracy. This obviously leaves little room for improvement. Do you suppose that all nine attributes are equally important to the task of classification? Is there possibly a subset of the nine attributes, when selected as input to naive Bayes, which leads to improved classification accuracy? Before determining the answers to these questions, let’s review WEKA’s approach to attribute selection. It’s not unusual for real-word data sets to contain irrelevant, redundant, or noisy attributes, which ultimately contribute to degradation in classification accuracy. In contrast, removing nonrelevant attributes often leads to improved classification accuracy. WEKA’s supervised attribute selection filter enables a combination of evaluation and search methods to be specified, where the objective is to determine a useful subset of attributes as input to a learning scheme.
Network before Mutation Node 1
Node 2
Node 3
W0A = - 0.1 W1A = 0.1 W1B = - 0.4
Node A
WAZ = - 0.6
W2A = 0.7 W2B = - 0.5 W3A = 0.4 W3B = 0.7
Node Z Node B
WBZ = 0.9 W0Z = -0.3
W0B = 0.1
Network after Mutation of Weights Incoming to Node B Node 1
Node 2
Node 3
W0A = - 0.1 W1A = 0.1 W1B = - 0.45
Node A
WAZ = - 0.6
W2A = 0.7 W2B = - 0.57 W3A = 0.4 W3B = 0.72
Node Z Node B
WBZ = 0.9 W0Z = -0.3
W0B = 0.1
Figure 6.7
Mutation in neural network weights.