The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM...

Classification of Text Document points towards associating one or more predefined categories based on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays an important role in training the system with predefined categories. The importance of Machine learning approach has felt because of which the study has been taken up for text document clas...

text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques ...

Text classification is the process of assigning text documents based on certain categories. A classifier is used to define the appropriate class for each text document based on the input algorithm used for classification. Due to the emerging trends in the field of internet and computers ,billions of text data are processed at a given time and so there is a need for organizing these data to prov...

Text mining has been employed in a wide range of applications such as text summarisation, text categorization, named entity extraction, and opinion and sentimental analysis. Text classification is the task of assigning predefined categories to free-text documents. That is, it is a supervised learning technique. While in text clustering (sometimes called document clustering) the possible categor...

Text classification has traditionally been one of the most popular problems in information retreival, natural language processing and machine learning. In the simplest case, the task of text classification [1] is as follows: A set of training documents T = {X1, X2, ...Xm} , each labelled with a class value from a set of k distinct labels, from the set {1, 2, ..k}, is used to learn a classificat...

Correct and efficient text classification is a major challenge in today’s world of rapidly increasing amount of accessible electronic text data. Kohonen networks have been applied to document classification with comparable success to other document clustering methods. An important challenge is to devise text similarity metrics that can improve the performance of text classification Kohonen netw...

The exploitation of the semantic relatedness kernel has always been an appealing subject in the context of text retrieval and information management. Typically, in text classification the documents are represented in the vector space using the bag-of-words (BOW) approach. The BOW approach does not take into account the semantic relatedness information. To further improve the text classification...