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The main finding shows that financial pressure helps to improve the volatility forecasting performance of the health care stock in both China and the USA. Empirical analysis further suggests that XGBoost performance outperforms other benchmark models, especially advanced machine learning models. This study also interprets predictions to help financial institutions and investors make correct decisions using Shapley additive explanations. The results illustrate that the prediction contribution of financial pressure is much stronger in China than in the USA. The prediction contribution distribution of the five-dimensional indicator of financial pressure in China is more discrete than in the USA. Different lag periods of financial pressure have an asymmetric predictive contribution to the volatility of the health care stock. The volatility of Chinese health care stock is mainly influenced by the five-dimensional indicator of financial pressure at the medium and late period lag but at the front and medium period lag for the USA. These findings are crucial for policymakers and investors in promoting the sustained health care stock market through financial pressure regulation.Keywords: Health care, Financial pressure, Machine learning, Shapley additive explanations.\n\n\n\nKeywords: Health care,\u00a0Financial pressure, Machine learning,\u00a0Shapley additive explanations.\n\n\n\n\n\u8d23\u4efb\u7f16\u8f91| \u90d1\u9648\u7490\u56fe\u6587\u7f16\u8f91| \u7fc1\u798f\u6dfb\u6392\u7248\u7f16\u8f91| \u9a6c\u8302\u6dc7 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Ensemble\u3001Balance Cascade\u7b49\u6539\u8fdb\u7684\u91c7\u6837\u7b97\u6cd5\uff1b\u5f53\u968f\u673a\u8fc7\u91c7\u6837\u53d1\u751f\u6a21\u578b\u8fc7\u62df\u5408\u95ee\u9898\u65f6\uff0c\u53ef\u91c7\u7528\u57fa\u4e8e\u6570\u636e\u5408\u6210\u7684\u8fc7\u91c7\u6837\u65b9\u6cd5SMOTE\uff0c\u901a\u8fc7KNN\u6280\u672f\u6a21\u62df\u751f\u6210\u5c11\u6570\u7c7b\u522b\u7684\u65b0\u6837\u672c\u5e76\u6dfb\u52a0\u5230\u6570\u636e\u96c6\u4e2d\uff1b\u91c7\u7528\u57fa\u4e8e\u805a\u7c7b\u7684\u91c7\u6837\u65b9\u6cd5\uff0c\u5148\u5206\u522b\u5bf9\u6b63\u8d1f\u4f8b\u6837\u672c\u8fdb\u884c\u805a\u7c7b\uff0c\u518d\u8fdb\u884c\u8fc7\u91c7\u6837\u6216\u6b20\u91c7\u6837\u65b9\u6cd5\uff0c\u89e3\u51b3\u7c7b\u95f4\u548c\u7c7b\u522b\u5185\u90e8\u4e0d\u5e73\u8861\u95ee\u9898\u3002\n\n\n\n2.\u7b97\u6cd5\u5c42\u9762\u7684\u4ee3\u4ef7\u654f\u611f\u578b\u5b66\u4e60\u3002\u901a\u8fc7\u8c03\u6574\u6837\u672c\u6743\u91cd\uff0c\u5bf9\u4e0d\u540c\u7c7b\u522b\u5206\u9519\u7684\u4ee3\u4ef7\u4e0d\u540c\uff0c\u5c06\u66f4\u591a\u7684\u8bef\u5206\u7c7b\u4ee3\u4ef7\u5206\u914d\u7ed9\u5c11\u6570\u7c7b\uff0c\u4ee5\u8fbe\u5230\u5bf9\u5c11\u6570\u7c7b\u66f4\u591a\u5173\u6ce8\u3002\u7528\u8d1d\u53f6\u65af\u98ce\u9669\u7406\u8bba\u6765\u89e3\u91ca\uff0c\u628a\u4ee3\u4ef7\u654f\u611f\u5b66\u4e60\u770b\u6210\u662f\u5206\u7c7b\u7ed3\u679c\u7684\u4e00\u79cd\u540e\u5904\u7406\uff0c\u6309\u7167\u4f20\u7edf\u65b9\u6cd5\u5b66\u4e60\u5230\u4e00\u4e2a\u6a21\u578b\uff0c\u4ee5\u5b9e\u73b0\u635f\u5931\u6700\u5c0f\u4e3a\u76ee\u6807\u5bf9\u7ed3\u679c\u8fdb\u884c\u8c03\u6574\u3002\n\n\n\n(\u4e94)\u6570\u636e\u53d8\u6362\n\n\n\n1.\u6570\u636e\u6807\u51c6\u5316\u3002\u7531\u4e8e\u6570\u636e\u91cf\u7eb2\u4e0d\u540c\uff0c\u5e38\u7528\u6700\u5c0f-\u6700\u5927\u6807\u51c6\u5316\u3001\u6807\u51c6\u5dee\u6807\u51c6\u5316\u7b49\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u6807\u51c6\u5316\u3002\n\n\n\n2.\u6d88\u51cf\u6570\u636e\u7ef4\u6570\u3002\u4e00\u662f\u4e3b\u6210\u5206\u5206\u6790\uff0c\u628a\u539f\u6765\u7684\u6307\u6807\u91cd\u65b0\u7ec4\u5408\u4e3a\u4e00\u7ec4\u65b0\u7684\u4e92\u76f8\u65e0\u5173\u7684\u51e0\u4e2a\u7efc\u5408\u6307\u6807\u6765\u4ee3\u66ff\u539f\u6709\u6307\u6807\uff0c\u540c\u65f6\u6839\u636e\u5b9e\u9645\u9700\u8981\u4ece\u4e2d\u9009\u53d6\u51e0\u4e2a\u5f71\u54cd\u8f83\u5927\u7684\u7efc\u5408\u6307\u6807\u6765\u8868\u793a\u539f\u6709\u6307\u6807\u7684\u4fe1\u606f\uff1b\u4e8c\u662f\u5c0f\u6ce2\u5206\u6790\u6cd5\uff0c\u901a\u8fc7\u5c0f\u6ce2\u53d8\u6362\u628a\u4e00\u4e2a\u6570\u636e\u5411\u91cf\u8f6c\u6362\u4e3a\u53e6\u4e00\u4e2a\u6570\u636e\u5411\u91cf\uff0c\u4e14\u8fd9\u4e24\u4e2a\u5411\u91cf\u7684\u957f\u5ea6\u76f8\u540c\u3002\u5b9e\u9645\u5e94\u7528\u4e2d\u4e00\u822c\u4f7f\u7528\u901a\u7528\u5c42\u6b21\u7b97\u6cd5(Hierarchical Pyramid Algorithm)\u5728\u6bcf\u6b21\u5faa\u73af\u65f6\u628a\u6570\u636e\u4e00\u5206\u4e3a\u4e8c\u5904\u7406\uff0c\u4ee5\u83b7\u5f97\u66f4\u9ad8\u7684\u8fd0\u7b97\u6548\u7387\u3002\n\n\n\n\n \u4e09\u3001\u793e\u4f1a\u7ecf\u6d4e\u7edf\u8ba1\u5927\u6570\u636e\u6280\u672f\u65b9\u6cd5\n\n\n\n\n\u672c\u8282\u4e3b\u8981\u4ecb\u7ecd\u5e38\u89c1\u7684\u9002\u7528\u4e8e\u793e\u4f1a\u7ecf\u6d4e\u7edf\u8ba1\u7684\u5927\u6570\u636e\u65b9\u6cd5\u53ca\u5b9e\u73b0\u8f6f\u4ef6\u7b80\u4ecb\u3002\n\n\n\n(\u4e00)\u7f51\u7edc\u5206\u6790\n\n\n\n\u793e\u4f1a\u7f51\u7edc(Social Network)\u662f\u7531\u793e\u4f1a\u5173\u7cfb\u6784\u6210\u7684\u7f51\u7edc\u7ed3\u6784\u3002\u5176\u4e2d\uff0c\u7f51\u7edc\u8282\u70b9\u8868\u793a\u793e\u4f1a\u884c\u52a8\u8005\uff0c\u901a\u5e38\u6307\u4e2a\u4f53\u6216\u7ec4\u7ec7\uff0c\u5982\u4e2a\u4eba\u3001\u516c\u53f8\u3001\u57ce\u5e02\u3001\u56fd\u5bb6\u7b49\uff0c\u7f51\u7edc\u8fde\u8fb9\u8868\u793a\u884c\u52a8\u8005\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u901a\u5e38\u6307\u8282\u70b9\u95f4\u7684\u5404\u79cd\u793e\u4f1a\u4e92\u52a8\u5173\u7cfb\uff0c\u5982\u670b\u53cb\u5173\u7cfb\u3001\u7ade\u4e89\u5173\u7cfb\u3001\u8d38\u6613\u5173\u7cfb\u7b49\uff0c\u6574\u5408\u800c\u6210\u7684\u7f51\u7edc\u7ed3\u6784\u5c06\u793e\u4f1a\u884c\u52a8\u8005\u901a\u8fc7\u5176\u793e\u4f1a\u5173\u7cfb\u8fde\u63a5\u4e32\u8054\u8d77\u6765\uff0c\u5f62\u6210\u793e\u4f1a\u7f51\u7edc\u3002\u76f8\u6bd4\u4f20\u7edf\u7edf\u8ba1\u65b9\u6cd5\u5e38\u7528\u4e8e\u201c\u5c5e\u6027\u6570\u636e\u201d\u7684\u5206\u6790\uff0c\u793e\u4f1a\u7f51\u7edc\u9002\u7528\u4e8e\u201c\u5173\u7cfb\u6570\u636e\u201d\u7684\u5206\u6790\uff0c\u901a\u8fc7\u6570\u5b66\u5206\u6790\u3001\u56fe\u8bba\u7b49\u5b9a\u91cf\u5206\u6790\u65b9\u6cd5\u7814\u7a76\u7f51\u7edc\u5173\u7cfb\uff0c\u6709\u52a9\u4e8e\u628a\u4e2a\u4f53\u95f4\u5173\u7cfb\u3001\u201c\u5fae\u89c2\u201d\u7f51\u7edc\u4e0e\u5927\u89c4\u6a21\u7684\u793e\u4f1a\u7cfb\u7edf\u7684\u201c\u5b8f\u89c2\u201d\u7ed3\u6784\u7ed3\u5408\u8d77\u6765\u3002\n\n\n\nR\u8bed\u8a00\u7684igraph\u5305\u53ef\u7528\u4e8e\u793e\u4f1a\u7f51\u7edc\u7684\u53ef\u89c6\u5316\u3001\u7f51\u7edc\u7279\u5f81\u5206\u6790\u7b49\u7814\u7a76\u3002\n\n\n\n(\u4e8c)\u7c97\u7cd9\u96c6\n\n\n\n\u7c97\u7cd9\u96c6\u7406\u8bba(Rough Set)\u662f\u4e00\u79cd\u7528\u4e8e\u5904\u7406\u4e0d\u786e\u5b9a\u6027\u548c\u4e0d\u7cbe\u786e\u6027\u77e5\u8bc6\u7684\u6570\u5b66\u5de5\u5177\u3002\u5176\u57fa\u672c\u601d\u60f3\u662f\u5728\u4fdd\u6301\u5206\u7c7b\u80fd\u529b\u4e0d\u53d8\u7684\u524d\u63d0\u4e0b\uff0c\u901a\u8fc7\u77e5\u8bc6\u7ea6\u7b80\u63d0\u53d6\u5206\u7c7b\u6216\u51b3\u7b56\u89c4\u5219\u3002\u5b83\u4ee5\u4e0d\u5b8c\u5168\u4fe1\u606f\u5904\u7406\u4e0d\u5206\u660e\u73b0\u8c61\uff0c\u6216\u4f9d\u636e\u89c2\u5bdf\u3001\u5ea6\u91cf\u5230\u7684\u67d0\u4e9b\u4e0d\u7cbe\u786e\u7ed3\u679c\u8fdb\u884c\u6570\u636e\u5206\u7c7b\u3002\n\n\n\n1.\u6570\u636e\u964d\u7ef4\u3002\u5c5e\u6027\u7ea6\u7b80\u6216\u77e5\u8bc6\u7ea6\u7b80\u662f\u7c97\u7cd9\u96c6\u7406\u8bba\u7684\u6838\u5fc3\u5185\u5bb9\u4e4b\u4e00\u3002\u5c5e\u6027\u7ea6\u7b80\u5728\u4fdd\u6301\u4fe1\u606f\u7cfb\u7edf\u5206\u7c7b\u80fd\u529b\u4e0d\u53d8\u7684\u60c5\u51b5\u4e0b\uff0c\u5220\u9664\u5197\u4f59\u53d8\u91cf\u3002\u968f\u7740\u5927\u6570\u636e\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u6570\u636e\u96c6\u7684\u89c4\u6a21\u53d8\u5f97\u8d8a\u6765\u8d8a\u5927\uff0c\u7c97\u7cd9\u96c6\u53ef\u901a\u8fc7\u65e0\u76d1\u7763\u5b66\u4e60\u5b9e\u73b0\u5bf9\u9ad8\u7ef4\u6570\u636e\u7684\u964d\u7ef4\u76ee\u7684\u3002\n\n\n\n2.\u4e8b\u52a1\u9879\u538b\u7f29\u3002\u7c97\u7cd9\u96c6\u7684\u5c5e\u6027\u7ea6\u7b80\u9488\u5bf9\u7684\u662f\u9ad8\u7ef4\u6570\u636e\u964d\u7ef4\uff0c\u5927\u6570\u636e\u7684\u4e8b\u52a1\u9879\u538b\u7f29\u5219\u662f\u901a\u8fc7\u6570\u636e\u5e93\u538b\u7f29\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\uff0c\u5c06\u4e00\u4e9b\u65e0\u5173\u6216\u591a\u4f59\u7684\u4fe1\u606f\u4e22\u6389\u800c\u4e0d\u5f71\u54cd\u5176\u539f\u6709\u7684\u529f\u80fd\u3002\u4f7f\u7528\u4fe1\u606f\u71b5\u4f5c\u4e3a\u4fe1\u606f\u6e90\u7edf\u8ba1\u91cf\u5ea6\uff0c\u538b\u7f29\u540e\u63d0\u4f9b\u7684\u4fe1\u606f\u91cf\u53ef\u4ee5\u53cd\u6620\u539f\u6709\u4fe1\u606f\u7cfb\u7edf\u3002\n\n\n\nR\u8bed\u8a00\u7684Roughsets\u5305\u53ef\u7528\u4e8e\u5b9e\u73b0\u7c97\u7cd9\u96c6\u65b9\u6cd5\u3002\n\n\n\n(\u4e09)\u6587\u672c\u6316\u6398\n\n\n\n\u6587\u672c\u6570\u636e\u6316\u6398\u662f\u4ece\u81ea\u7136\u8bed\u8a00\u6587\u672c\u4e2d\u6316\u6398\u7528\u6237\u6240\u611f\u5174\u8da3\u7684\u6a21\u5f0f\u548c\u77e5\u8bc6\u7684\u6280\u672f\uff0c\u5176\u96be\u70b9\u5728\u4e8e\u5bf9\u975e\u7ed3\u6784\u5316\u81ea\u7136\u8bed\u8a00\u6587\u672c\u5185\u5bb9\u7684\u5206\u6790\u548c\u7406\u89e3\u3002\u901a\u5e38\u5229\u7528\u667a\u80fd\u7b97\u6cd5\u62bd\u53d6\u6216\u6807\u8bb0\u5173\u952e\u5b57\u8bcd\u3001\u5b57\u8bcd\u95f4\u7684\u5173\u7cfb\uff0c\u5e76\u6309\u7167\u5185\u5bb9\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u7c7b\u6216\u805a\u7c7b\uff0c\u83b7\u53d6\u6709\u7528\u7684\u6a21\u5f0f\u548c\u77e5\u8bc6\u3002\n\n\n\n1.\u6587\u672c\u6570\u636e\u9884\u5904\u7406\u3002\u9996\u5148\u5bf9\u975e\u7ed3\u6784\u5316\u6570\u636e\u8fdb\u884c\u521d\u6b65\u6e05\u6d17\uff0c\u5982\u53bb\u91cd\u3001\u7f3a\u5931\u503c\u5904\u7406\u3001\u65e0\u6548\u5b57\u7b26\u8fc7\u6ee4\u7b49\uff1b\u7136\u540e\u5bf9\u6587\u672c\u5206\u8bcd\uff0c\u4e3b\u8981\u65b9\u6cd5\u6709\u57fa\u4e8e\u8bcd\u8868\u7684\u5206\u8bcd\u65b9\u6cd5(\u5982\u6b63\u5411\u6700\u5927\u5339\u914d\u6cd5)\u3001\u57fa\u4e8e\u7edf\u8ba1\u6a21\u578b\u7684\u5206\u8bcd\u65b9\u6cd5(\u5982N-gram\u8bed\u8a00\u6a21\u578b)\u3001\u57fa\u4e8e\u5e8f\u5217\u6807\u6ce8\u7684\u5206\u8bcd\u65b9\u6cd5(\u5982\u9690\u9a6c\u5c14\u79d1\u592b\u6a21\u578b)\u3002\u5e38\u7528\u5206\u8bcd\u5de5\u5177\u6709\u4e2d\u79d1\u9662\u7684ICTCLAS\u3001jieba\u7b49\uff0cICTCLAS\u7b97\u6cd5\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u8bcd\u5178\uff0c\u5bf9\u65b0\u8bcd\u3001\u4eba\u540d\u3001\u5730\u540d\u7b49\u7684\u53d1\u73b0\u5177\u6709\u826f\u597d\u6548\u679c\uff0c\u5bf9\u5e94R\u8bed\u8a00\u7684Rwordseg\u5305\uff1bjieba\u5206\u8bcd\u5de5\u5177\u8bcd\u6027\u6807\u6ce8\u65b9\u4fbf\uff0c\u5177\u6709\u66f4\u5feb\u7684\u5206\u8bcd\u901f\u5ea6\uff0c\u5bf9\u5e94R\u8bed\u8a00\u7684jiebaR\u5305\u3002\n\n\n\n2.\u6587\u672c\u8868\u793a\u3002\u6587\u672c\u8868\u793a\u662f\u7528\u6587\u672c\u7684\u7279\u5f81\u96c6\u5408\u6765\u4ee3\u8868\u539f\u59cb\u6587\u672c\u7684\u8fc7\u7a0b\u3002\n\n\n\n(1)\u79bb\u6563\u5f0f\u6587\u672c\u8868\u793a\u3002\u4e00\u662fOne-Hot\u7f16\u7801\uff0c\u5c06\u8bcd\u8868\u793a\u6210\u4e00\u4e2a\u5411\u91cf\uff0c\u5411\u91cf\u4e2d\u5f53\u524d\u8bcd\u7684\u4f4d\u7f6e\u7684\u503c\u4e3a1\uff0c\u5176\u4f59\u7684\u4f4d\u7f6e\u4e3a0\uff0c\u5f97\u5230\u9ad8\u7ef4\u7a00\u758f\u77e9\u9635\uff1b\u4e8c\u662f\u8bcd\u888b\u6a21\u578b(Bag of Words)\uff0c\u5229\u7528\u8bcd\u9891\u6765\u5efa\u7acb\u5411\u91cf\uff1b\u4e09\u662fTF-IDF(Term Frequency-Inverse Document Frequency)\uff0c\u53ef\u533a\u5206\u5e38\u7528\u8bcd\u548c\u4e13\u6709\u540d\u8bcd\u5bf9\u6587\u672c\u7684\u91cd\u8981\u6027\u3002\n\n\n\n(2)\u5206\u5e03\u5f0f\u6587\u672c\u8868\u793a\u3002\u4e3b\u8981\u5305\u62ec\u57fa\u4e8e\u77e9\u9635\u7684\u5206\u5e03\u8868\u793a(\u5982\u5171\u73b0\u77e9\u9635(Cooccurrence Matrix)\u3001\u57fa\u4e8e\u805a\u7c7b\u7684\u5206\u5e03\u8868\u793a(\u5982\u5e03\u6717\u805a\u7c7b(Brown Clustering))\u548c\u57fa\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u5206\u5e03\u8868\u793a(\u5982\u795e\u7ecf\u7f51\u7edc\u8bed\u8a00\u6a21\u578b(Neural Network Language Model))\u3002\u76ee\u524d\u5206\u5e03\u5f0f\u6587\u672c\u8868\u793a\u4e2d\u7684\u8bcd\u5d4c\u5165(Word Embedding)\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5b9e\u8df5\u65b9\u9762\u7684\u53d6\u5f97\u6781\u4f73\u7684\u8868\u73b0\uff0c\u5e38\u7528\u8bcd\u5d4c\u5165\u6a21\u578b\u5305\u62ecWord2vec\u548cBERT(Bidirectional Encoder Representation from Transformers)\u3002Word2vec\u53ef\u4ee5\u4f7f\u7528Python\u7684gensim\u6a21\u5757\u5b9e\u73b0\uff1bBERT\u7684PyTorch\u5b9e\u73b0\u53ef\u4ee5\u4f7f\u7528Python\u7684torch\u6a21\u5757\u3002\n\n\n\n3.\u4e3b\u8981\u5e94\u7528\u3002\n\n\n\n(1)\u6587\u672c\u5206\u7c7b\u3002\u6839\u636e\u4e00\u4e2a\u5df2\u7ecf\u88ab\u6807\u6ce8\u7684\u8bad\u7ec3\u6587\u672c\u96c6\u5408\u627e\u5230\u6587\u672c\u7279\u5f81\u548c\u6587\u672c\u7c7b\u522b\u4e4b\u95f4\u7684\u5173\u7cfb\u6a21\u578b\uff0c\u5e76\u5229\u7528\u6b64\u6a21\u578b\u5bf9\u65b0\u6587\u672c\u8fdb\u884c\u7c7b\u522b\u5224\u65ad\u3002\u5e38\u7528\u7684\u6587\u672c\u5206\u7c7b\u7b97\u6cd5\u5305\u62ec\uff1a\u6734\u7d20\u8d1d\u53f6\u65af(Naive Bayes model)\u3001Logistic\u56de\u5f52\u3001\u6700\u5927\u71b5\u6a21\u578b(Maximum Entropy)\u3001K-\u6700\u8fd1\u90bb(K-Nearest Neighbor)\u3001\u652f\u6301\u5411\u91cf\u673a(Support Vector Machine)\u3001\u51b3\u7b56\u6811(Decision Tree)\u4ee5\u53ca\u4ee5\u5377\u79ef\u795e\u7ecf\u7f51\u7edc(Convolutional Neural Networks)\u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc(Recurrent Neural Network)\u4e3a\u4ee3\u8868\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc(Deep Neural Network)\u7b49\u3002\n\n\n\n(2)\u6587\u672c\u805a\u7c7b\u3002\u5c06\u7ed9\u5b9a\u6587\u672c\u96c6\u6309\u7167\u67d0\u79cd\u7279\u5f81\u5212\u5206\u4e3a\u4e0d\u540c\u7c7b\u522b\uff0c\u53ef\u63d0\u4f9b\u5927\u89c4\u6a21\u6587\u6863\u96c6\u5185\u5bb9\u7684\u603b\u62ec\uff0c\u8bc6\u522b\u9690\u85cf\u7684\u6587\u6863\u95f4\u7684\u76f8\u4f3c\u5ea6\u7b49\u3002\u5177\u4f53\u7b97\u6cd5\u5305\u62ec\uff1aK-\u5747\u503c\u805a\u7c7b(K-means Clustering)\u3001\u5355\u904d\u805a\u7c7b(Single-pass Clustering)\u3001\u5c42\u6b21\u805a\u7c7b(Hierarchical Clustering)\u3001\u57fa\u4e8e\u5bc6\u5ea6\u7684\u805a\u7c7b(Density-based Clustering)\u3001\u57fa\u4e8e\u7f51\u683c\u7684\u805a\u7c7b(Grid-based Clustering)\u3001\u5b50\u7a7a\u95f4\u805a\u7c7b(Subspace Clustering)\u3001\u57fa\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u805a\u7c7b\u3001\u56fe\u805a\u7c7b(Graph clustering)\u3001\u8c31\u805a\u7c7b(Spectral Clustering)\u548c\u540e\u7f00\u6811\u805a\u7c7b(Suffix Tree Clustering)\u7b49\u3002\n\n\n\n(3)\u4e3b\u9898\u6a21\u578b\u3002\u5c06\u6587\u672c\u5411\u91cf\u4ece\u9ad8\u7ef4\u8bcd\u9879\u7a7a\u95f4\u6620\u5c04\u5230\u4e00\u4e2a\u4f4e\u7ef4\u8bed\u4e49\u7a7a\u95f4\uff0c\u6316\u6398\u9690\u542b\u5728\u8bcd\u6c47\u80cc\u540e\u7684\u4e3b\u9898\u548c\u6982\u5ff5\u3002\u5e38\u7528\u6a21\u578b\u5305\u62ec\uff1a\u6f5c\u5728\u8bed\u4e49\u5206\u6790(Latent Semantic Analysis)\u3001\u6982\u7387\u6f5c\u5728\u8bed\u4e49\u5206\u6790(Probabilistic Latent Semantic Analysis)\u548c\u6f5c\u5728\u72c4\u514b\u96f7\u5206\u5e03(Latent Dirichlet Allocation)\u7b49\u3002\n\n\n\n(4)\u60c5\u611f\u5206\u6790\u4e0e\u89c2\u70b9\u6316\u6398\u3002\u5bf9\u5e26\u6709\u60c5\u611f\u8272\u5f69\u7684\u4e3b\u89c2\u6027\u6587\u672c\u8fdb\u884c\u63d0\u53d6\u3001\u5206\u6790\u3001\u5904\u7406\u3001\u5f52\u7eb3\u548c\u63a8\u7406\uff0c\u6839\u636e\u6587\u672c\u6240\u8868\u8fbe\u7684\u89c2\u70b9\u548c\u6001\u5ea6\u7b49\u4e3b\u89c2\u4fe1\u606f\u8fdb\u884c\u5206\u7c7b\u3002\n\n\n\n(\u56db)\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\n\n\n\n\u6df1\u5ea6\u5b66\u4e60\u7ecf\u5e38\u5e94\u7528\u4e8e\u5404\u79cd\u76d1\u7763\u6a21\u578b\u7684\u8bc6\u522b\u95ee\u9898\uff0c\u5305\u542b\u8f93\u5165\u5c42\u3001\u8f93\u51fa\u5c42\u4ee5\u53ca\u591a\u4e2a\u9690\u85cf\u5c42\u3002\u4f20\u7edf\u7684\u591a\u5c42\u611f\u77e5\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u7684\u53cd\u5411\u4f20\u64ad(BP)\u7b97\u6cd5\u4e3a\u6838\u5fc3\u7b97\u6cd5\u3002\n\n\n\n1.\u81ea\u7f16\u7801\u673a(AE)\u4e0e\u9650\u5236\u6027\u73bb\u5c14\u5179\u66fc\u673a(RBM)\u3002\u5176\u57fa\u672c\u539f\u7406\u662f\u57fa\u4e8e\u975e\u76d1\u7763\u5b66\u4e60\u627e\u5230\u6570\u636e\u5185\u5728\u89c4\u5f8b\u7684\u7279\u5f81\u8868\u793a\uff0c\u7136\u540e\u7528\u4e8e\u76d1\u7763\u5b66\u4e60\u7684\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u4e2d\u3002\u81ea\u7f16\u7801\u673a\u7684\u7f51\u7edc\u7ed3\u6784\u6709\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u4e24\u90e8\u5206\uff0c\u5c06\u8f93\u5165\u4fe1\u606f\u4f5c\u4e3a\u5b66\u4e60\u76ee\u6807\u8fdb\u884c\u7279\u5f81\u5b66\u4e60\u53ca\u964d\u7ef4\u3002\u9650\u5236\u6027\u73bb\u5c14\u5179\u66fc\u673a\u662f\u901a\u8fc7\u5efa\u7acb\u6982\u7387\u5206\u5e03\u548c\u80fd\u91cf\u51fd\u6570\u95f4\u7684\u5173\u7cfb\uff0c\u6c42\u89e3\u51fa\u80fd\u91cf\u51fd\u6570\uff0c\u5e76\u4e14\u523b\u753b\u51fa\u6570\u636e\u5185\u5728\u7684\u89c4\u5f8b\u3002\u65b9\u6cd5\u5b9e\u73b0\u4e3b\u8981\u91c7\u7528Python\uff0c\u591a\u5c42\u81ea\u7f16\u7801\u5668\u662f\u9996\u5148\u5bfc\u5165tensorflow\u548cDense\u6a21\u5757\uff0c\u901a\u8fc7Dense\u6784\u9020\u7f16\u7801\u5c42\u548c\u89e3\u7801\u5c42\uff0c\u7136\u540e\u901a\u8fc7Model\u642d\u5efa\u7f16\u7801\u6a21\u578b\uff0c\u6700\u540e\u8bad\u7ec3\u81ea\u7f16\u7801\u5668\uff1b\u9650\u5236\u6027\u73bb\u5c14\u5179\u66fc\u673a\u53ef\u4ee5\u901a\u8fc7sklearn.neural_network\u5bfc\u5165\u3002\n\n\n\n2.\u5377\u79ef\u795e\u7ecf\u7f51\u7edc(CNN)\u3002CNN\u4e00\u822c\u5305\u542b\u964d\u91c7\u6837\u5c42\u3001\u5377\u79ef\u5c42\u3001\u5168\u8fde\u63a5\u5c42\u4ee5\u53ca\u8f93\u51fa\uff0c\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u662f\u5377\u79ef\u5c42\u7684\u4f5c\u7528\u3002\u91c7\u7528CNN\u65b9\u6cd5\u628a\u4e00\u53e5\u8bdd\u8f6c\u5316\u6210\u4e8c\u7ef4\u77e9\u9635\uff0c\u5377\u79ef\u6838\u7684\u5bbd\u5ea6\u9009\u7528\u8bcd\u5411\u91cf\u7ef4\u6570\uff0c\u5bf9\u77e9\u9635\u8fdb\u884c\u5377\u79ef\u64cd\u4f5c\uff0c\u4ece\u800c\u4ece\u53e5\u5b50\u4e2d\u63d0\u53d6\u5173\u952e\u8bcd\u8bed\u3001\u8bcd\u7ec4\u7279\u5f81\u3002\u901a\u8fc7R\u8bed\u8a00\u52a0\u8f7dkeras\u3001caret\u5305\uff0c\u5bfc\u5165\u6570\u636e\u96c6\uff0c\u5b9a\u4e49\u548c\u62df\u5408\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u3002\n\n\n\n3.\u9012\u5f52\u795e\u7ecf\u7f51\u7edc(RNN)\u3002RNN\u662f\u5bf9\u5e8f\u5217\u578b\u6570\u636e\u8fdb\u884c\u5efa\u6a21\uff0c\u9700\u8981\u4fdd\u7559\u5e8f\u5217\u4e0a\u4e0b\u6587\u7684\u4fe1\u606f\uff0c\u6240\u4ee5\u5b83\u7684\u9690\u8282\u70b9\u4e2d\u5b58\u5728\u53cd\u9988\u73af\uff0c\u5373\u5f53\u524d\u65f6\u523b\u7684\u9690\u542b\u8282\u70b9\u503c\u4e0d\u4ec5\u548c\u5f53\u524d\u8282\u70b9\u7684\u8f93\u5165\u6709\u5173\u7cfb\uff0c\u8fd8\u4e0e\u524d\u4e00\u65f6\u523b\u7684\u9690\u542b\u8282\u70b9\u503c\u6709\u5173\u7cfb\u3002\u53ef\u5229\u7528\u957f\u77ed\u65f6\u8bb0\u5fc6\u6a21\u578b(LSTM)\u6765\u6539\u8fdbRNN\u68af\u5ea6\u6d88\u6563\u73b0\u8c61\u3002\u4f7f\u7528R\u8bed\u8a00\u5b89\u88c5\u5e76\u8fd0\u884crnn\u5305\u3001digest\u5305\uff0c\u5e76\u8bbe\u7f6e\u7f51\u7edc\u53c2\u6570\uff0c\u5373\u53ef\u5b9e\u73b0RNN\u3002\n\n\n\n4.\u751f\u6210\u5bf9\u6297\u7f51\u7edc(GAN)\u3002GAN\u4e0d\u4f9d\u8d56\u4efb\u4f55\u5206\u5e03\u5047\u8bbe\uff0c\u4ee5\u7b80\u5355\u7684\u65b9\u5f0f\u4ece\u6f5c\u5728\u7a7a\u95f4\u751f\u6210\u771f\u5b9e\u7684\u6837\u672c\u3002\u53ef\u4f7f\u7528Python\u5b9e\u73b0\uff0c\u5728tensorflow\u6846\u67b6\u4e0b\u5bfc\u5165keras\uff0c\u4f7f\u7528keras.Sequential\u642d\u5efa\u751f\u6210\u5668\u6a21\u578b\u548c\u8fa8\u522b\u5668\u6a21\u578b\uff0c\u901a\u8fc7\u5b9a\u4e49\u6279\u6b21\u8bad\u7ec3\u51fd\u6570\u5b9e\u73b0\u53c2\u6570\u8bad\u7ec3\u3002\n\n\n\n5.\u6df1\u5ea6\u8fc1\u79fb\u5b66\u4e60\u3002\u8fc1\u79fb\u5b66\u4e60\u662f\u5229\u7528\u76f8\u5173\u9886\u57df\u77e5\u8bc6\u6765\u63d0\u9ad8\u5b66\u4e60\u6027\u80fd\uff0c\u53ef\u5145\u5206\u5229\u7528\u4e4b\u524d\u6807\u6ce8\u597d\u7684\u6570\u636e\uff0c\u540c\u65f6\u53c8\u4fdd\u8bc1\u65b0\u4efb\u52a1\u6a21\u578b\u7cbe\u5ea6\u3002\u6df1\u5ea6\u8fc1\u79fb\u5b66\u4e60\u5b9e\u73b0\u4e3b\u8981\u91c7\u7528Python\u3002\u9996\u5148\uff0c\u5728tensorflow\u6846\u67b6\u4e0b\uff0c\u5bfc\u5165keras\u3001numpy\u548cmatplotlib\u7b49\u76f8\u5173\u6a21\u5757\uff1b\u7136\u540e\uff0c\u901a\u8fc7class\u7c7b\u5c01\u88c5\u7f51\u7edc\u7ed3\u6784\uff0c\u4f7f\u7528model.compile\u914d\u7f6e\u8bad\u7ec3\u65b9\u6cd5\uff1b\u6700\u540e\uff0c\u5728model.fit()\u4e2d\u6267\u884c\u8bad\u7ec3\u8fc7\u7a0b\u3002\n\n\n\n(\u4e94)\u96c6\u6210\u7b97\u6cd5\n\n\n\n\u96c6\u6210\u5b66\u4e60(Ensemble Learning)\u662f\u7528\u4e8e\u8bad\u7ec3\u591a\u4e2a\u5b66\u4e60\u5668\u5e76\u7ec4\u5408\u8f93\u51fa\u7684\u8fc7\u7a0b\u3002\u5176\u57fa\u672c\u601d\u60f3\u662f\u8ba4\u8bc6\u5230\u73b0\u5b9e\u4e16\u754c\u4e2d\u6bcf\u4e2a\u6a21\u578b\u90fd\u6709\u5176\u5c40\u9650\u6027\uff0c\u5e76\u4e14\u53ef\u80fd\u4f1a\u51fa\u9519\uff0c\u96c6\u6210\u5b66\u4e60\u7684\u76ee\u7684\u662f\u7ba1\u7406\u5b83\u4eec\u7684\u4f18\u52bf\u548c\u52a3\u52bf\uff0c\u6700\u7ec8\u505a\u51fa\u6700\u4f73\u51b3\u7b56\u3002\n\n\n\n\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4e00\u662f\u63d0\u5347\u6cd5(Boosting)\uff0c\u5efa\u7acb\u5728\u5176\u5b83\u5206\u7c7b\u7684\u57fa\u7840\u4e0a\u4e3a\u6bcf\u4e2a\u8bad\u7ec3\u6837\u672c\u96c6\u5206\u914d\u4e00\u4e2a\u6743\u91cd\uff0c\u6700\u7ec8\u5408\u5e76\u5230\u6a21\u578b\u4e2d\u3002\u5176\u4e2d\uff0cXGBoost\u7b97\u6cd5\u5728\u5206\u7c7b\u6548\u679c\u3001\u4e1a\u52a1\u89e3\u91ca\u6027\u3001\u5efa\u6a21\u6548\u7387\u7b49\u65b9\u9762\u83b7\u5f97\u4e86\u4e1a\u754c\u5e7f\u6cdb\u7684\u8ba4\u53ef\uff1b\u4e8c\u662f\u5957\u888b\u6cd5(Bagging)\uff0c\u5bf9\u6240\u6709\u57fa\u5b66\u4e60\u5668\u4e00\u81f4\u5bf9\u5f85\uff0c\u5728\u5927\u90e8\u5206\u60c5\u51b5\u4e0b\uff0c\u7ecf\u8fc7bagging\u5f97\u5230\u7684\u7ed3\u679c\u65b9\u5dee\u66f4\u5c0f\uff1b\u4e09\u662f\u5806\u53e0\u6cd5(Stacking)\uff0c\u6838\u5fc3\u601d\u60f3\u662f\u901a\u8fc7\u589e\u52a0\u57fa\u5b66\u4e60\u5668\u7684\u5f02\u8d28\u6027\u548c\u4f7f\u7528\u5143\u5b66\u4e60\u7ec4\u5408\u57fa\u5b66\u4e60\u5668\u9884\u6d4b\u7684\u7ed3\u679c\u6765\u51cf\u5c11\u6cdb\u5316\u8bef\u5dee\u3002\n\n\n\n\u96c6\u6210\u7b97\u6cd5\u7684\u5b9e\u73b0\u4ee3\u7801\u53ef\u67e5\u8be2R\u8bed\u8a00\u7684mlr\u5305\u548cmlbench\u5305\u3002\n\n\n\n(\u516d)\u6b63\u5219\u5316\u6a21\u578b\n\n\n\n\u6b63\u5219\u5316\u53ef\u4ee5\u8fdb\u884c\u7cfb\u6570\u538b\u7f29\u4f30\u8ba1\uff0c\u662f\u4e00\u79cd\u5e38\u7528\u7684\u5927\u6570\u636e\u6280\u672f\u65b9\u6cd5\u3002\u793e\u4f1a\u7ecf\u6d4e\u7edf\u8ba1\u4e2d\u5e38\u5e38\u9047\u5230\u9ad8\u7ef4\u5c0f\u6837\u672c\u6570\u636e\uff0c\u6b64\u7c7b\u6570\u636e\u7684\u7279\u70b9\u662f\u81ea\u53d8\u91cf\u7ef4\u5ea6\u8fdc\u8fdc\u5927\u4e8e\u6837\u672c\u91cf\uff0c\u6570\u636e\u5305\u542b\u8bb8\u591a\u4e0e\u56e0\u53d8\u91cf\u65e0\u5173\u7684\u5197\u4f59\u53d8\u91cf\uff0c\u5f71\u54cd\u6a21\u578b\u7684\u89e3\u91ca\u529b\u5ea6\u548c\u4f30\u8ba1\u7cbe\u5ea6\uff0c\u4e14\u6837\u672c\u91cf\u5c0f\u5bb9\u6613\u5bfc\u81f4\u8fc7\u62df\u5408\u95ee\u9898\uff0c\u964d\u4f4e\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u548c\u7a33\u5b9a\u6027\u3002\u6b63\u5219\u5316\u662f\u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316\u7b56\u7565\u7684\u5b9e\u73b0\uff0c\u8be5\u65b9\u6cd5\u57fa\u4e8e\u5168\u90e8\u89e3\u91ca\u53d8\u91cf\u5efa\u6a21\uff0c\u5728\u7ecf\u9a8c\u98ce\u9669\u4e0a\u52a0\u6b63\u5219\u5316\u9879(\u60e9\u7f5a\u9879)\uff0c\u901a\u8fc7\u5bf9\u7cfb\u6570\u52a0\u60e9\u7f5a\u7ea6\u675f\u5c06\u7cfb\u6570\u4f30\u8ba1\u503c\u5f80\u96f6\u7684\u65b9\u5411\u538b\u7f29\u3002\u5e38\u7528\u7684\u6b63\u5219\u5316\u65b9\u6cd5\u6709\u5cad\u56de\u5f52\u3001Lasso\u4ee5\u53ca\u57fa\u4e8eVAR(p)\u7684Lasso\u65b9\u6cd5\u3002\n\n\n\n(\u4e03)\u8d1d\u53f6\u65af\u65b9\u6cd5\u4e0e\u6df1\u5ea6\u5b66\u4e60\n\n\n\n\u8d1d\u53f6\u65af\u6df1\u5ea6\u5b66\u4e60\u662f\u7ed3\u5408\u6df1\u5ea6\u5b66\u4e60\u548c\u8d1d\u53f6\u65af\u6a21\u578b\u7684\u6982\u7387\u6846\u67b6\uff0c\u901a\u8fc7\u5bf9\u6587\u672c\u6216\u56fe\u50cf\u3001\u97f3\u9891\u7b49\u7684\u611f\u77e5\u63d0\u5347\u66f4\u9ad8\u5c42\u6b21\u63a8\u7406\u7684\u80fd\u529b\uff0c\u5b9e\u73b0\u53cc\u5411\u53cd\u9988\u548c\u63a8\u65ad\u3002\n\n\n\n1.\u8d1d\u53f6\u65af\u7f51\u7edc\u3002\u4f5c\u4e3a\u4e00\u79cd\u4e0d\u786e\u5b9a\u6027\u7684\u56e0\u679c\u63a8\u65ad\u6a21\u578b\uff0c\u8d1d\u53f6\u65af\u7f51\u7edc\u7684\u62d3\u6734\u7ed3\u6784\u662f\u4e00\u4e2a\u6709\u5411\u65e0\u73af\u56fe(DAG)\uff0c\u6bcf\u4e2a\u8282\u70b9\u4ee3\u8868\u4e00\u4e2a\u968f\u673a\u53d8\u91cf\uff0c\u8282\u70b9\u4e4b\u95f4\u7684\u8fb9\u4ee3\u8868\u968f\u673a\u53d8\u91cf\u95f4\u7684\u6982\u7387\u4f9d\u8d56\u5173\u7cfb\u3002\u5c06\u6709\u56e0\u679c\u5173\u7cfb\u7684\u53d8\u91cf\u7528\u7bad\u5934\u6765\u8fde\u63a5\uff0c\u82e5\u4e24\u4e2a\u8282\u70b9\u95f4\u4ee5\u4e00\u4e2a\u5355\u7bad\u5934\u8fde\u63a5\u5728\u4e00\u8d77\uff0c\u8868\u793a\u5176\u4e2d\u4e00\u4e2a\u8282\u70b9\u662f\u201c\u56e0\u201d\uff0c\u53e6\u4e00\u4e2a\u662f\u201c\u679c\u201d\uff0c\u4e24\u8282\u70b9\u5c31\u4f1a\u4ea7\u751f\u4e00\u4e2a\u6761\u4ef6\u6982\u7387\u503c\u3002\u5728\u6982\u7387\u56fe\u4e2d\uff0c\u7528\u8d1d\u53f6\u65af\u7f51\u7edc\u6216\u9a6c\u5c14\u79d1\u592b\u968f\u673a\u573a\u8f6c\u6362\u4e3a\u56e0\u5b50\u56fe\u53d8\u91cf\uff0c\u518d\u7ed3\u5408Sum-product\u7b49\u7b97\u6cd5\u9ad8\u6548\u5730\u6c42\u89e3\u5404\u53d8\u91cf\u7684\u8fb9\u7f18\u5206\u5e03\u3002\n\n\n\n2.\u8d1d\u53f6\u65af\u5206\u7c7b\u53ca\u56de\u5f52\u6811\u3002\u591a\u5143\u7edf\u8ba1\u4e2d\u5f88\u591a\u7ecf\u5178\u7684\u56de\u5f52\u6a21\u578b\u3001\u4e3b\u6210\u5206\u5206\u6790\u3001\u56e0\u5b50\u5206\u6790\u3001\u7ed3\u6784\u65b9\u7a0b\u6a21\u578b\u3001\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