Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td align="left">Parameters name</td><td align="center">Value </td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td align="left">Maximum number of iterations</td><td align="center">100</td></tr><tr><td align="left">Population size</td><td align="center">20</td></tr><tr><td align="left">Crossover probability</td><td align="center">0.4</td></tr><tr><td align="left">Mutation probability</td><td align="center">0.01</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Mathematical Problems in Engineering

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Table 3

Table 3: Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features 

Table 3 | Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features 