# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Content-Based Approaches  
Currently, the document classification community is slightly biased when it comes to the data exploitation of research papers to categorize or classify them. Most of the contemporary approaches rely on the content of research articles due to the richness of features which can be constructed by exploiting the whole content. This section focuses on content-based state-of-the-art approaches. In 2016, [PERSON] et al. [25] proposed a novel Bayesian automatic text classification approach by exploiting different content-based features. They proposed a class-dependent set of features. They formulated classification rules by harnessing [PERSON]'s PDF Projection Theorem for the conversion of class-specific PDFs in low-dimensional feature into raw data space. They have also presented another approach based on a feature selection framework for Naive Bayes [21]. These selected features are ranked for the classification. They presented a new divergence measure which is called \"Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multilabel classification\". Another study by [PERSON] et al. [22] is based on the survey of features' selection approaches for text classification. They highlighted the existing feature selection schemes and different methods of reducing the dimension of these features. These methods are categorized into two categories, (1) wrapper and (2) filter. The filter scheme provides significant performance improvement over the wrapper scheme without classifier feedback. The filter scheme has been used in most of the text classification/categorization problems in the literature. [PERSON] et al. built a content-based classifier by using Naive Bayes and Logistic Regression algorithms [26]. The classifier was built by relying on different features from which Bi-grams feature outperformed for both datasets. Similarly, [PERSON] et al. 's [23] approach classified research papers based on different features by applying semantic similarity to them. Another content-based classification and visualization of scientific documents is proposed by [PERSON] et al. [27]. All modules of this approach were implemented by using the madjS system. The madjS system provides data evaluation functionalities via an extended relational database. The automatic clustering approach of scientific text and newspapers articles was proposed by [PERSON] and [PERSON] [28]. A content level approach was proposed by [PERSON] et al. [13] for the classification of scientific documents. They applied different algorithms such as: Naive Bayes, decision tree, k-nearest neighbor (KNN), neural network, Support Vector Machine (SVM) for the classification of documents. Likewise, [PERSON] et al. [29] proposed a fuzzy ontology to represent and reason for fuzzy or vague information for more fine-grained classification by incorporating the fuzziness features. [PERSON] and [PERSON] [30] presented an automatic subject-indexing approach for digital libraries and repositories. They proposed a concept matching approach by identifying these concepts from the documents. Then, concept similarities are computed with the documents. Concept similarity is used for the classification of documents. [PERSON] et al. [12] proposed a document classification algorithm based on the Latent Dirichlet Allocation (LDA) [31] and unlabeled dataset. The algorithm of this approach assigns one topic to one class label. A query extension method is proposed by [PERSON] et al. [32], which extended information related to research papers by using their cited references. The evaluation of this approach was conducted on biomedical documents of the PubMed database. Another content-based hierarchical classification technique of textual data is presented by the authors of [33]. They proposed a classifier that was based on a modified version of the well-known k-nearest neighbor classifier (K-NN). The original classifier works only with the category representatives instead of the training documents.