本文選題：關(guān)聯(lián)性 + 信息融合�。� 參考：《工程科學(xué)與技術(shù)》2017年03期

【摘要】：針對人體面部皮膚狀態(tài)指標(biāo)與中醫(yī)體質(zhì)類型之間的關(guān)聯(lián)性進(jìn)行科學(xué)、定量研究,從測試數(shù)據(jù)持續(xù)累積與知識發(fā)現(xiàn)深入推進(jìn)的過程視角,嘗試揭示人體內(nèi)在中醫(yī)體質(zhì)與外觀皮膚狀態(tài)指標(biāo)間的復(fù)雜動態(tài)演化規(guī)律。綜合小樣本條件下決策樹的良好歸納特性及大樣本條件下貝葉斯算法分類準(zhǔn)確率高的優(yōu)勢。提出基于建模數(shù)據(jù)量會不斷增多的趨勢,構(gòu)建可自適應(yīng)修訂決策樹和模糊樸素貝葉斯融合分類算法的權(quán)重,以適用于測試數(shù)據(jù)從小到大積累過程中分類模型均具有較好分類特性及可解釋性的應(yīng)用要求。其中決策樹采用最佳后剪枝方式,避免了常規(guī)決策樹存在的過擬合弊端;樸素貝葉斯算法則通過定義指標(biāo)歸屬區(qū)間的模糊隸屬度來解決皮膚屬性測試與分類中存在的隨機(jī)性與模糊性。實(shí)證結(jié)果表明本文提出的分類模型的融合權(quán)重可動態(tài)調(diào)整且隨著建模數(shù)據(jù)的增多分類精度會相應(yīng)提高。目前對應(yīng)151個建模數(shù)據(jù)的分類模型的分類準(zhǔn)確率為86.7%,高于獨(dú)立決策樹、樸素貝葉斯的83.3%和80%,亦高于對照組80個建模數(shù)據(jù)對應(yīng)分類準(zhǔn)確率的76.7%。分析可得:此皮膚與體質(zhì)動態(tài)分類模型通過有效利用參與建模的數(shù)據(jù)信息,能識別出人體面部外觀皮膚狀態(tài)指標(biāo)與內(nèi)在中醫(yī)體質(zhì)之間的復(fù)雜關(guān)聯(lián)性,建立的分類模型具有較好的精度與可解釋性,為基于數(shù)據(jù)驅(qū)動的中醫(yī)理論的科學(xué)化、智能化發(fā)展進(jìn)行了有益的探索。
[Abstract]:In view of the relationship between human facial skin state index and TCM physique type, a scientific and quantitative study was carried out from the perspective of continuous accumulation of test data and further promotion of knowledge discovery. This paper attempts to reveal the complex dynamic evolution law of human body between TCM physique and appearance skin state index. The good inductive property of decision tree under small sample condition and the advantage of high classification accuracy of Bayesian algorithm under large sample condition are synthesized. Based on the trend that the amount of modeling data will increase, the weight of adaptive revisable decision tree and fuzzy naive Bayes fusion classification algorithm is constructed. In order to apply to the process of test data accumulation from small to large, the classification model has better classification characteristics and interpretable application requirements. The decision tree adopts the best post-pruning method to avoid the over-fitting drawback of the conventional decision tree. Naive Bayesian algorithm solves the randomness and fuzziness of skin attribute testing and classification by defining the fuzzy membership degree of index attribution interval. The empirical results show that the fusion weight of the proposed classification model can be dynamically adjusted and the classification accuracy will be improved with the increase of modeling data. At present, the classification accuracy of the classification model corresponding to 151 modeling data is 86.7, which is higher than that of independent decision tree, 83.3% and 80% of naive Bayes, and 76.7% of the corresponding classification accuracy of 80 modeling data in the control group. The analysis shows that the dynamic classification model of skin and physique can identify the complex relationship between the skin state index of human face and TCM physique by effectively using the data information involved in the modeling. The established classification model has good precision and interpretability, which is a useful exploration for the scientific and intelligent development of data-driven TCM theory.
【作者單位】： 北京工商大學(xué)計算機(jī)與信息工程學(xué)院食品安全大數(shù)據(jù)技術(shù)北京市重點(diǎn)實(shí)驗(yàn)室;北京工商大學(xué)中國化妝品研究中心;
【基金】：北京市教育委員會科技發(fā)展計劃重點(diǎn)項(xiàng)目(KZ201510011011) 北京工商大學(xué)促進(jìn)人才培養(yǎng)綜合改革項(xiàng)目(19005428069/007);北京工商大學(xué)研究生創(chuàng)新基金
【分類號】：R229;TP181
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本文編號：1929699