BOOKMARK CLASSIFICATION in .NET framework

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BOOKMARK CLASSIFICATION
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This module produces weight-term pairs given a dataset. Each encountered word in a URL or title eld is assigned a weight according to the metric used. Several metrics were implemented for this purpose:
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Boolean existential metric. All keywords that exist in the document are given a weight of 1, those that are absent are assigned 0 [301]. Frequency count metric. The normalized frequency of the keywords in the document is used as the weight [302]. TF-IDF . The term frequency-inverse document frequency metric [303] assigns higher weights to those keywords that occur frequently in the current document but not in most others. It is calculated using the N formula w(t, i) = Fi (t) log Nt , where Fi (t) is the frequency of term t in document i, N is the number of documents in the collection, and Nt is the total number of documents that contain t.
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For the example bookmark, the keywords {java, sun, com, performance} are obtained from the URL, and the keywords {ways, increase, java, performance} from the title eld. By way of the simple Boolean existential metric, the vector elements relating to these keywords will each contain the value 1, the remainder 0. The resulting sets of weight-term pairs, no matter which keyword acquisition metric is adopted, are large in size and need to be greatly reduced to be of any practical use for classi cation. Hence the next step is dimensionality reduction.
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11.3.2.2 Dimensionality Reduction Given the weight-term sets, this module aims to signi cantly reduce their size while retaining their information content and preserving the semantics of those remaining keywords. FRFS is applied here to achieve this goal. Once a reduct has been calculated, the dataset can then be reduced by deleting those attributes that are absent from the reduct. The reduced dataset is now in a form that can be used by the classi cation module. Returning to the example, it may be decided by this module that the term com provides little or no useful information. The column relating to this term is removed from the main dataset. This process is repeated for all keywords deemed by FRFS to be information poor. 11.3.2.3 Classi cation This module attempts to classify a given bookmark or bookmarks using the reduced keyword datasets obtained by the feature selection stage. Each bookmark has been transformed into a weight-term vector by the keyword acquisition process. For investigation purposes, two different inference techniques were implemented to perform classi cation: the Boolean inexact model and the vector space model. To illustrate the operation of the classi cation methods, consider the training and testing vectors presented in Figure 11.2. The training data consists of two objects, one classi ed to the Sport category and the other classi ed to News. Values in the vector represent the frequency of occurrence of terms in the training item.
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APPLICATIONS II: WEB CONTENT CATEGORIZATION
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Figure 11.2 Training and testing vectors
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For BIM classi cation, the training data (viewed as rules) is used to classify the new object by comparing term values in the vectors and incrementing a score if they are the same. Classifying using the rst object results in a score of 10/12, as 10 of the term values match this training object. With the second object a score of 7/12 is produced. Hence, for this example, the test object is classi ed to the Sport category. In VSM, the similarity measure de ned in equation (11.1) is used to determine the closeness of the test object the training examples. For the rst training item, to the computed similarity (5/ 6 6) = 0.83. For the second item, the similarity is is calculated to be (4/ 7 6) = 0.62. Again, the test object is determined to belong to the Sport category.
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