MULTILABEL OVER-SAMPLING AND UNDER-SAMPLING WITH CLASS ALIGNMENT FOR IMBALANCED MULTILABEL TEXT CLASSIFICATION

Simultaneous multiple labelling of documents, also known as multilabel text classification, will not perform optimally if the class is highly imbalanced. Class imbalanced entails skewness in the fundamental data for distribution that leads to more difficulty in classification. Random over-sampling and under-sampling are common approaches to solve the class imbalanced problem. However, these approaches have several drawbacks; the under-sampling is likely to dispose of useful data, whereas the over-sampling can heighten the probability of overfitting. Therefore, a new method that can avoid discarding useful data and overfitting problems is needed. This study proposes a method to tackle the class imbalanced problem by combining multilabel over-sampling and under-sampling with class alignment (ML-OUSCA). In the proposed ML-OUSCA, instead of using all the training instances, it draws a new training set by over-sampling small size classes and under-sampling big size classes. To evaluate our proposed ML-OUSCA, evaluation metrics of average precision, average recall and average F-measure on three benchmark datasets, namely, Reuters-21578, Bibtex, and Enron datasets, were performed. Experimental results showed that the proposed ML-OUSCA outperformed the chosen baseline random resampling approaches; K-means SMOTE and KNN-US. Thus, based on the results, we can conclude that designing a resampling method based on the class imbalanced together with class alignment will improve multilabel classification even better than just the random resampling method.

Not Seeing the Forest for the Trees: The Impact of Multiple Labelling on Consumer Choices for Olive Oil

Multiple quality labels that signal whether a particular food has special characteristics relating to geographical origin or production method have become standard within European food policy. The aim of this paper was to investigate how two of these labels in particular influence consumers’ food choices. We assessed consumers’ preferences for an extra virgin olive oil (EVOO) displaying EU quality labels and focus on whether they are complements or substitutes. In order to do so, we used a discrete choice experiment (DCE) to estimate main and two-way interactions effects with data from a self-administrated survey in a Spanish region. Results indicate that while consumers positively value both the Protected Designation of Origin (PDO) and the organic labels, the valuation for PDO is almost double that of the valuation of the organic label. Furthermore, the findings show that for a majority of consumers considered both labels substitutes, while a small group considered them complements. These findings can help producers identify an optimal labelling strategy to maximize returns on certification investments.