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⾦金金数据是如何鉴 的 姜 鹏⻩黄
此处省略略558个字……
当优秀⼯工具被⽤用于「邪恶」的⽬目的,后果好可怕!😱
Milestone I
(⼈人⼯工识别)
Milestone II
(关键字匹配)
⽂文本信息
⽂文字处理理
OCR
关键字匹配
分类与
权重
⾃自动封停 正常表单
嫌疑表单
表单原始信息提取
https://github.com/tesseract-ocr/tesseract
OCR(Optical character recognition)
⽂文字处理理
正.品.9.9.9神油*成.⼈人+⽤用.品只卖*6.6.元(包.邮)
正品神油成⼈人⽤用品只卖元包邮
去除符号
保留留汉字
(⼩小-姨-⼦子-的-⾃自-述)
⼩小姨⼦子的⾃自述
如何能减少⼈人⼯工⼲干预
- 不不需要⼈人⼯工设置权重
- 不不需要⼈人⼯工选择关键词
- 能越来越准确
Machine Learning
Milestone III
(机器器学习)
训练集
机器器学习算法
分类器器新数据 预测
标
记
监督学习(Supervised Learning)
算法选择 结果评估特征提取
分类问题(⽂文本)
我喜欢⾦金金数据
训练集 标记
⼩小姨⼦子到家我玩 ?
预测
我喜欢⼩小姨⼦子
我喜欢Ruby
正常
正常
⾊色情
⽂文本 -> 数学:计数
Ruby 喜欢 ⼩小姨⼦子 ⾦金金数据 标记
我 喜欢 ⾦金金数据 正常
我 喜欢 Ruby 正常
我 喜欢 ⼩小姨⼦子 ⾊色情
⼩小姨⼦子 到 我家 玩
特征
样本
0 1 0 1
11 0 0
10 1 0
00 1 0
0
0
1
?
⽂文本 -> 数学:权重
TF-IDF:term frequency–inverse document frequency
衡量量词语对⽂文档的重要程度:

• ⼀一个词在⽂文档中的出现次数成正⽐比

• 与该词在整个语⾔言中的出现次数成反⽐比
0 1 0 1
1 1 0 0
0 1 1 0
0 0.5085 0 0.861
0.861 0.5085 0 0
0 0.5085 0.861 0
['ruby', '喜欢', '⼩小姨⼦子', ‘⾦金金数据'] ['ruby', '喜欢', '⼩小姨⼦子', '⾦金金数据']
⽂文本 -> 数学:分词
⽂文本 -> 数学:计数
⽂文本 -> 数学:权重
「⼀一切问题都是数学问题」
分类问题(⽂文本)
我喜欢⾦金金数据
训练集 标记
⼩小姨⼦子到家我玩 ?
预测
我喜欢⼩小姨⼦子
我喜欢Ruby
正常
正常
⾊色情
0 0.5085 0 0.861
0.861 0.5085 0 0
0 0.5085 0.861 0
分类问题(数学)
0
0
1
0 0 0.861 0 ?
训练集 标记
预测
算法选择 结果评估特征提取
⽀支持向量量机(SVM)
训练
预测:⾊色情
预测:正常
算法选择 结果评估特征提取
评估
准确率 =
预测正确样本数
总预测数
?
Predict
⾊色情 正常
Actual
⾊色情 6(TP) 4(FN)
正常 0(FP) 90(TN)
精确率(Precision)
召回率(Recall)
评估:混淆矩阵(Confusion Matrix)
预测是⾊色情的结果中,确实是⾊色情的⽐比例例
所有⾊色情表单中,预测是⾊色情的⽐比例例
系统集成
Goldendata
Server
(Ruby on Rails)
DB
ML Server
(Python Flask)
训练学习
内容预测
存储分类器器
Content Check
Server
(Ruby on Rails)
表单封停
⽤用户通知
训练集管理理
3. 获取训练集
4. 获取分类结果
1. 表单存储
2. 表单提取
Matrix
Predict
⾊色情 政治 欺诈 正常
Actual
⾊色情 92 0 0 2
政治 0 56 0 9
欺诈 0 0 70 7
正常 1 2 5 2254
评估
Precision Recall F-score
⾊色情 0.989247 0.978723 0.983957
政治 0.965517 0.861538 0.910569
欺诈 0.933333 0.909090 0.921052
正常 0.989247 0.978723 0.983957
训练集
机器器学习算法
分类器器新数据 预测
标
记
What’s Next?
扩充样本
提升质量量
SVM
Decision Tree
Naive Bayes
K-Neighbors
…
Ensemble Learning
• Voting Classifier
• Random Forest
…
总结
⼈人⼯工识别 -> 关键字匹配 -> 机器器学习

特征选择 -> 算法选择 -> 结果评估

⾦金金数据实现
数据来源:公开发布的表单
Thanks

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金数据是如何鉴黄的