The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Text mining is the process of extracting interesting information and knowledge from unstructured text. One key difficulty with text classification learning algorithms is that they require many hand-labele...

Text classification and feature selection plays an important role for correctly identifying the documents into particular category, due to the explosive growth of the textual information from the electronic digital documents as well as world wide web. In the text mining present challenge is to select important or relevant feature from large and vast amount of features in the data set. The aim o...

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...

and Objective Nearly at every patient visit medical documents are produced and stored in a medical record, often in unstructured form as free text. Growing amount of stored documents increases the need for effective and timely retrieval of information. We developed a multi-label classification system to categorize German language free text medical documents (e.g. discharge letters, clinical fin...

In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents. We present a theoretical argume...

We consider scalability issues of the text classification problem where by using (multi)-labeled training documents, we try to build classifiers that assign documents into classes permitting classification in multiple classes. A new class of classification problems; called ‘scalable’, is introduced, with applications on web mining. Scalable classification utilizes newly classified instances in ...