Question: How to improve learning algorithms' accuracy in presence of imbalanced data in this paper?
Evidence: We consider the dataset consisting of the entire collection of articles of the Wikipedia Medicine Portal, updated at the end of 2014. Wikipedia articles are written according to the Media Wiki markup language, a HTML-like language. Among the structural elements of one page, which differs from standard HTML pages, there are i) the internal links, i.e., links to other Wikipedia pages, different from links to external resources); ii) categories, which represent the Media Wiki categories a page belongs to: they are encoded in the part of text within the Media Wiki “categories" tag in the page source, and iii) informative boxes, so called “infoboxes", which summarize in a structured manner some peculiar pieces of information related the topic of the article. The category values for the articles in the medical portal span over the ones listed at https://en.wikipedia.org/wiki/Portal:Medicine. Examples of categories, which appear at the bottom of each Wikipedia page, are in Fig. 1 .
Infoboxes of the medical portal feature medical content and standard coding. As an example, Fig. 2 shows the infobox in the Alzheimer's disease page of the portal. The infobox contains explanatory figures and text denoting peculiar characteristics of the disease and the value for the standard code of such disease (ICD9, as for the international classification of the disease).
Thanks to WikiProject Medicine, the dataset of articles we collected from the Wikipedia Medicine Portal has been manually labeled into seven quality classes. They are ordered as Stub, Start, C, B, A, Good Article (GA), Featured Article (FA). The Featured and Good article classes are the highest ones: to have those labels, an article requires a community consensus and an official review by selected editors, while the other labels can be achieved with reviews from a larger, even controlled, set of editors. Actually, none of the articles in the dataset is labeled as A, thus, in the following, we do not consider that class, restricting the investigation to six classes.
At the date of our study, we were able to gather 24,362 rated documents. Remarkably, only a small percentage of them (1%) is labeled as GA and FA. Indeed, the distribution of the articles among the classes is highly skewed. There are very few (201) articles for the highest quality classes (FA and GA), while the vast majority (19,108) belongs to the lowest quality ones (Stub and Start). This holds not only for the medical portal. Indeed, it is common in all Wikipedia, where, on average, only one article in every thousand is a Featured one.
In Section "Experiments and results" , we will adopt a set of machine-learning classifiers to automatically label the articles into the quality classes. Dealing with imbalanced classes is a common situation in many real applications of classification learning: healthy patients over the population, fraudulent actions over daily genuine transactions, and so on. Without any countermeasure, common classifiers tend to correctly identify only articles belonging to the majority classes, clearly leading to severe mis-classification of the minority classes, since typical learning algorithms strive to maximize the overall prediction accuracy. To reduce the disequilibrium among the size of the classes, we have first randomly sampled the articles belonging to the most populated classes. Then, we have performed some further elaboration, as shown in the following.
Many studies have been conducted to improve learning algorithms accuracy in presence of imbalanced data BIBREF8 . For the current work, we have considered one of the most popular approaches, namely the Synthetic Sampling with Data Generation, detailed in BIBREF9 . It consists in generating synthetic instances from the minority classes, to balance the overall dataset. The approach has been broadly applied to problems relying on NLP features, see, e.g., BIBREF10 . In our case, we resampled the input data set by applying the Synthetic Minority Oversampling TEchnique (SMOTE), with percentage 40% for GA and 180%, for FA. In particular, the steps to oversample are the following:
Table 1 shows the number of articles in the dataset, divided per class, as well as the random samples we have considered for our study. The experiments presented in Section "Experiments and results" are based on the articles of the right-hand column in the table.
Many studies have been conducted to improve learning algorithms accuracy in presence of imbalanced data [15]. For the current work, we have considered one of the most popular approaches, namely the Synthetic Sampling with Data Generation, detailed in [9].
Answer: The authors used the Synthetic Sampling with Data Generation to improve learning algorithms accuracy in presence of imbalanced data.