Abstract
The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed a great competence of the proposed WELM compared to the ELM.
Highlights
Through the growing of social networks, a huge quantity of text data is quickly generated, the need for a well-defined methodology to analyze and classify these voluminous data has drawn many communities' attention to this kind of data which is known as unstructured data [1]
From the results we can see the order of effective features weights as follows Term Frequency- inverse Document Frequency (TF-IDF), Thematic Words (TW) and Term Frequency (TF)
The results showed the competence of the weighted Extreme Learning Machine (WELM) compared with Extreme Learning Machine (ELM)
Summary
Through the growing of social networks, a huge quantity of text data is quickly generated, the need for a well-defined methodology to analyze and classify these voluminous data has drawn many communities' attention to this kind of data which is known as unstructured data [1]. This phenomenon which made the importance of text classification begins to spring up. TC gives a solution in arranging innumerable articles and papers. TC can help to filter out the annoying emails automatically by classifying these text emails as a spam [3]
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