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decision_tree_learning [2018/08/14 07:25] mwpark 만듦 |
decision_tree_learning [2018/11/13 07:49] mwpark |
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- CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing | - CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing | ||
- MARS: extends decision trees to handle numerical data better. | - MARS: extends decision trees to handle numerical data better. | ||
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+ | |||
+ | # Automatic Web Content Extraction by Combination of Learning and Grouping | ||
+ | |||
+ | Created: Nov 09, 2018 6:38 PM | ||
+ | Tags: Paper | ||
+ | |||
+ | # 1. INTRODUCTION | ||
+ | |||
+ | - The main content in a webpage is often accompanied by a lot of additional and often distracting content such ad branding banners, navigation elements, advertisements and copyright etc. | ||
+ | - The web pages in the World Wide Web are highly heterogeneous. | ||
+ | - Previous work | ||
+ | - Heuristic | ||
+ | - Template based Approach | ||
+ | - TED | ||
+ | |||
+ | # 2. RELATED WORK | ||
+ | |||
+ | - CETR | ||
+ | - CETD | ||
+ | - VIPS | ||
+ | |||
+ | # 3. PROBLEM FORMULATION AND SOLUTION | ||
+ | |||
+ | ![](Untitled-c3f13dfd-e5f1-486c-aaf6-4580e50223b5.png) | ||
+ | |||
+ | # 4. FEATURE SELECTION | ||
+ | |||
+ | $$F_x(v_i)=F' | ||
+ | |||
+ | ## 4.1 Position and Area Features | ||
+ | |||
+ | - We consider the left, right, top, bottom, horizontal center and vertical center positions. | ||
+ | |||
+ | $$POS\_LEFT = 1 - |BEST\_LEFT\_LEFT|$$ | ||
+ | |||
+ | ## 4.2 Font Features | ||
+ | |||
+ | $$FONT\_COLOR\_POPULARITY=\sum_i\varphi_{ki} \varphi_{ri}$$ | ||
+ | |||
+ | $$FONT\_SIZE=\sum_i{\rho_{ki}(z_i-z_{min}) \over (z_{max}-z_{min})}$$ | ||
+ | |||
+ | ## 4.3 Text, Tag and Link Features | ||
+ | |||
+ | $$TEXT\_RATIO={A_{text} \over A_{text} +A_{image} + 1}$$ | ||
+ | |||
+ | $$TAG\_DENSITY={numTags \over numChars+1}$$ | ||
+ | |||
+ | $$LINK\_DENSITY={numLinks \over numTags+1}$$ | ||
+ | |||
+ | # 5 LEARNING | ||
+ | |||
+ | # 6 GROUPING AND REFINING | ||
+ | |||
+ | 1. Grouping | ||
+ | 2. Group Selection | ||
+ | 3. Refining | ||
+ | 4. EXPERIMENTAL EVALUATION | ||
+ | 1. Evaluation Data Set and Metrics | ||
+ | 2. Comparison with the Baseline Methods | ||
+ | - LR_A | ||
+ | - SVM_A | ||
+ | - LR | ||
+ | - SVM | ||
+ | - MSS | ||
+ | 3. Parameter Sensitivity Analysis | ||
+ | 5. CONCLUSIONS |