decision_tree_learning
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decision_tree_learning [2018/11/13 07:49] mwpark |
decision_tree_learning [2021/04/13 06:54] |
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| - | * Algorithms | ||
| - | - ID3 (Iterative Dichotomiser 3) | ||
| - | - C4.5 (successor of ID3) | ||
| - | - CART (Classification And Regression Tree) | ||
| - | - CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing | ||
| - | - MARS: extends decision trees to handle numerical data better. | ||
| - | |||
| - | # 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 | ||
| - | |||
| - |  | ||
| - | |||
| - | # 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 | ||
decision_tree_learning.txt · 마지막으로 수정됨: 2021/04/13 06:54 (바깥 편집)
