本文選題：隨機(jī)森林　切入點(diǎn)：犯罪風(fēng)險(xiǎn)預(yù)測(cè)　出處：《華東師范大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年04期

【摘要】：犯罪預(yù)測(cè)是犯罪預(yù)防的前提,也是公安部門亟待解決的問題.隨機(jī)森林作為一種組合分類方法,具有準(zhǔn)確率高、速度快、性能穩(wěn)定的特性,且能夠給出指標(biāo)重要性評(píng)價(jià),本文將其應(yīng)用于犯罪風(fēng)險(xiǎn)預(yù)測(cè)中.實(shí)驗(yàn)證明,隨機(jī)森林方法選出的指標(biāo)集可以顯著地提高預(yù)測(cè)準(zhǔn)確率,基于該方法構(gòu)建的預(yù)測(cè)模型相較于神經(jīng)網(wǎng)絡(luò)與支持向量機(jī)具有更高的準(zhǔn)確性和穩(wěn)定性,能夠滿足犯罪風(fēng)險(xiǎn)預(yù)測(cè)的需求.
[Abstract]:Crime prediction is the premise of crime prevention and an urgent problem to be solved by public security departments.As a combined classification method, stochastic forest has the characteristics of high accuracy, fast speed, stable performance, and can give the evaluation of index importance. This paper applies it to crime risk prediction.The experimental results show that the index set selected by the stochastic forest method can significantly improve the prediction accuracy. The prediction model based on this method is more accurate and stable than the neural network and support vector machine.Can satisfy the demand of crime risk forecast.
【作者單位】： 華東師范大學(xué)地理科學(xué)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金人才培養(yǎng)項(xiàng)目(J1310028)
【分類號(hào)】：D917;TP18
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本文編號(hào)：1694719