## 2 State of the Art  
In text classification, most of the studies deal with flat classification, when it is assumed that there are no relationships between the categories. There are two basic hierarchical classification methods, namely, the big-bang approach and the top-down level-based approach [5]. In the big-bang approach, a document is assigned to a class in one single step, whereas in the top-down level-based approach, classification is performed with the classifiers built at each level of a hierarchy. In the top-down level-based approach, a classification problem is decomposed into a set of smaller problems corresponding to hierarchical splits in a tree. First, classes are distinguished at the top level, and then the lower level distinctions are determined only within the subclasses at the appropriate top-level class. Each of these sub-problems can be solved much more accurately as well [6, 7]. Moreover, a greater accuracy is achievable because classifiers can identify and ignore commonalities between the subtopics of a specific class, and concentrate on those features that distinguish them [8]. This approach is used by most hierarchical classification methods due to its simplicity [5; 6; 9; 10; 11]. They utilize the known hierarchical (taxonomy) structure built by experts. One of the obvious problems with the top-down approach is that misclassification at a higher level of a hierarchy may force a document to be wrongly routed before it gets classified at a lower level. Another problem is that sometimes there is no predefined hierarchy and one has first to build it. It is usually built from data or from data labels. We address the latter problem, which seems to us not that computationally complex, since the number of labels is usually less than the number of data attributes. In our research, we follow the top-down level based approach utilizing a hierarchical topic structure to break down the problem of classification into a sequence of simpler problems. There are approaches implying linear discriminant projection of categories to create hierarchies based on their similarities: [12; 13]. They show that classification performance gets better as compared with a flat case. There is a range of methods aimed to reduce the complexity of training flat classifiers. Usually they partition data into two parts and create a two-level hierarchy, e.g., [14]. The HOMER method [15] constructs a Hierarchy Of Multi-label classifiERs, each one dealing with a much smaller set of labels with respect to \(|L|\) and with a more balanced example distribution. This leads to an improved estimated performance along with linear training and logarithmic testing complexities with respect to \(|L|\). At the first step, the HOMER automatically organizes labels into a tree-shaped hierarchy. This is accomplished by recursively partitioning a set of labels into a number of nodes using the balance clustering algorithm. Then it builds one multi-label classifier at each node apart from the leaves. In the PHOCS, we use the same concept of hierarchy and meta-labels. [PERSON] _et al._[16] also introduce the RA\(k\)EL classifier (RAndom \(k\) labELsets, \(k\) is a parameter specifying the size of labelsets) that outperforms some well-known multi-label classifiers. In the recent work [17], the authors used datasets with predefined hierarchies and tried to guess them, but did not construct a hierarchy that could be good for classification. Generally, this is a more challenging task than a standard hierarchical multi-label classification, when classifiers are based on a known class hierarchy. In our research, we pursue a similar objective. In 2011 the second Pascal challenge on large-scale classification was held. [PERSON] _et al._[3] got the first place in two of the three benchmarks. They enhanced the flat kNN method and outperformed hierarchical classification. They used an interesting method of building a hierarchy as well, which employs only existing labels without the use of any meta-labels. The performance was lower than in the flat case.