Feature selection method based on binary quantum particle swarm algorithm
A feature selection method, quantum particle swarm technology, applied in the direction of calculation, calculation model, gene model, etc., can solve the problem of large amount of calculation, achieve good classification accuracy, increase diversity, and improve the effect of diversity
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[0031] Such as figure 1 As shown, a feature selection method based on the binary quantum particle swarm algorithm, the specific steps are as follows:
[0032] Step 1. Input the public dataset Lymphoma, where the number of samples is 45, the number of features is 4026, the number of negative samples is 22, and the number of positive samples is 23.
[0033] Step 2. Using the maximum information coefficient (MIC) to calculate the correlation between all features and class labels. The calculation method of MIC is shown in formula (1) (2).
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[0036] Step 3. The features are sorted according to the relevance of the MIC value, and some weakly correlated features are deleted according to the set threshold.
[0037] Step 4. Use the binary particle swarm optimization algorithm to search and optimize the remaining features to obtain the optimal feature subset. For the specific algorithm flow chart, see figure 2 .
[0038] In the BQPSO algorithm, there is no c...
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