Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning

<table class="table-group" id="tab11"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Journal evaluation indicator</td><td class="align_center">Number</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">H-index</td><td class="align_center">8</td></tr><tr><td class="align_left">Immediacy index</td><td class="align_center">8</td></tr><tr><td class="align_left">Number of papers</td><td class="align_center">3</td></tr><tr><td class="align_left">Average number of citations</td><td class="align_center">3</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>If the number of times an indicator ranks in the top five is greater than three under the other eight methods except the input perturbation method, the indicator and its number of times will be shown in this table.</div>

Scientific Programming

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Table 11

Table 11: Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning 