With the continuous increase in available data, there is a pressing need to organize it and modern classification problems often involve the prediction of multiple labels simultaneously associated with a single instance. Known as Multi-Label Classification, it is one such task that is omnipresent in many real-world problems.
In this project,i have experimented with various deep learning models and evaluated them for the task of Multi-label text classification. To form a comprehensive representation of the texts i have investigate a powerful and intelligent feature extraction technique based on deep representation, which is GloVe. To improve the classification system, LSTM is the most natural sequence processing model capable of learning long-term dependencies and remembering past data. As a result, i have presented a comparative analysis of several recent architectures model based LSTM.
The use of fusing the prediction of all classifiers or selected classifiers in this work was suggested in anticipation of better performance, regardless of the base used and to better generalize our contribution.
The Ohsumed collection is a subset of the MEDLINE database, which is a bibliographic database of important peer-reviewed medical literature maintained by the National Library of Medicine. This dataset is multi-label text related problem type. The subset we consider is the collection consisting of the first 20,000 documents from the 50,216 medical abstracts of the year 1991. The classification scheme consists of the 23 Medical Subject Headings (MeSH) categories of cardiovascular diseases. After selecting such category subset, the document number is 13,929. The main task was to classify those categories where the documents can belong to several classes, for example, an abstract can belong to four types of diseases (Classes: C17, C1, C3, C23). This data collection is available at http://disi.unitn.it/moschitti/corpora.htm.
Dataprepar.ipynb This file is for preparing and converting the dataset from Files to csv forma. if you want the test and train in the same file just merge it.
