Abstract
Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively.
Highlights
The ever-growing size and complexity of unstructured information available on the World WideWeb continuously motivate computer science researchers in the development of more useful data description models and retrieval methods
In practice, information retrieval systems do make mistakes: false negatives are retrieved and displayed, they should be rejected, while false positive images are not presented to a user by a retrieval system, they should be [27,28]
Lift charts are a type of charts, such as the receiver operating characteristic (ROC) curves and precision-recall curves, which are often utilized in machine learning for visualization and evaluation of classification models
Summary
The ever-growing size and complexity of unstructured information available on the World Wide. The second application of lift charts, which is very important and often overlooked, is in the classification of ranked retrieval results Such results may be optimized for either precision or recall. This trend is encouraged by the growth in the variety and complexity of, and in demands for intensity and precision in, the experimentation by the researchers They are invaluable tools in their field of practice, the affective multimedia databases have two important drawbacks that can be at least alleviated, if not completely eliminated, by utilizing methods from computer science and information retrieval. These drawbacks are demanding and time-consuming construction and retrieval of affective multimedia documents Both are related to inefficient search of the databases, which is caused by their rudimentary and inadequate semantic representation model. The two most common models of emotion are the pleasure-arousal-dominance (PAD) dimensional [21] and discrete models [22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
