| Título: | A FUZZY CHARACTERISTIC SELECTION AND EXTRACTION FOR TEXT CATEGORIZATION |
| Autores: |
Koorapati, Jhansi; M.Tech Student, Dept of CSE, CMR Technical Campus,
Kandlakoya (V)
Medchal (M), A.P, India Lakshmi, G.Vijaya; M.Tech Student, Dept of CSE, CMR Technical Campus, Kandlakoya (V) Medchal (M), A.P, India Nagaraju, R.; Asst. Professor, Dept of CSE, CMR Technical Campus, Kandlakoya (V) Medchal (M), A.P, India |
| Fecha: | 2013-06-02 |
| Publicador: | International journal of computer and electronics research |
| Fuente: |
 |
| Tipo: | Peer-reviewed Article |
| Tema: | Dimensionality; Clustering; Feature Selection; Text Classification; High Dimensional; Data Sets |
| Descripción: | The dimensionality of the characteristic vector is generally enormous in text classification. Such high dimensionality can be a ruthless obstacle for classification algorithms. To reduce the dimensionality of feature vectors for text classification a powerful method called feature clustering is used. A fuzzy similarity-based self-constructing algorithm for feature clustering is proposed in this paper. Based on similarity test the words in the feature vector of a document set are grouped into clusters. Words that are related to each other are grouped into the same cluster. Each cluster is distinguished by a membership function with statistical mean and divergence. A desired number of clusters are formed automatically when all the words have been fed in and we have one extracted feature for each cluster. The extracted feature which is equivalent to a cluster is a weighted combination of the words contained in the cluster. By this algorithm, the resulting membership functions match closely with and describe properly the real sharing of the training data. Besides, for determining the appropriate number of extracted features which can then be avoided as the user need not specify the number of extracted features in advance, and trial-and-error. Our simulation results show that our technique can run faster and attain better extracted features than other methods. |
| Idioma: | Inglés |
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