Changes between Version 3 and Version 4 of MultiLabelClassification


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Timestamp:
05/05/11 14:40:23 (3 years ago)
Author:
wencanluo
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  • MultiLabelClassification

    v3 v4  
    33=== Method 1: add a special prefix to each labels === 
    44Add a special prefix to each class label and set the optional flag to be ‘meta’. For example, there are a four-label data set, “Sports”, “Religion”, ”Science”, ”Politics”, respectively. Then we can name their attribute names as “_c_Sports”, “_c_Religion”, ” _c_Science”, ” _c_Politics”. With this flag, we can deal the labels. 
     5 
    56What needs to be changed? 
    67* [//doc/reference/tabdelimited.htm tab file] 
    78* Methods of [//doc/reference/Example.htm Example], like getClass() 
    89Problems to be solved: 
    9 * Whenever it visits the class attributes, the code should search all the attributes to locate the attributes that have prefix “_c_” 
     10* Whenever it visits the class attributes, the code should search all the attributes to locate the attributes that have prefix “_c_”. This problems can be solved by adding some flag to indicate which attributes are class type. 
    1011 
    1112=== Method 2: using special Attribute value ===  
     
    1617=== Method 4: allow to multi 'class' optional flags ===  
    1718Now the [//doc/reference/tabdelimited.htm tab file] can have only at most one 'class' flag. We can allow several attributes to be 'class'. 
     19 
    1820What needs to be changed? 
    1921* [//doc/reference/ExampleTable.htm ExampleTable]: add a vector to store all the class's names 
     
    5052 
    5153== Timeline == 
    52  April 25 – May 23 (Before official coding time) :: To discuss the details about my ideas with my mentor to archive a final agreement, including designing the dataset support, choosing which transformation and adaptive methods to implement. Based on the final agreement, I will write some testing to make clear about all goals. 
     54 April 25 – May 23 (Before official coding time) :: To discuss the details about my ideas with my mentor to archive a final agreement, including designing the dataset support, choosing which transformation and adaptive methods to implement. Familiar with structure of Orange source code. How the Python code and C++ code combined? Based on the final agreement, I will write some testing code to make clear about all goals. 
    5355 May 23 – June 18 (Official coding period starts) :: Start to design the framework to support multi-label classification, including the multi-label data structure –instance, instances, attribute, evaluator, etc. 
    5456 Coding on designing basic multi-label dataset, two of problem-transformation methods-Binary relevance (BR), Calibrated label ranking (CLR), one GUI widget, and two evaluation measures: Example-based Hamming-Loss, Classfication Accuracy, Precision, Recall; Label-based.