H.6.3.1. Classifier design and evaluation
M. Moradi; J. Hamidzadeh
Abstract
Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ...
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Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions affected by environmental conditions and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. To evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross validation over real-world data sets. Experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding applied evaluation metrics.
H.6.3.1. Classifier design and evaluation
Z. Mirzamomen; Kh. Ghafooripour
Abstract
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships ...
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Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases can bring about significant improvements. In this paper, we have introduced positive, negative and hybrid relationships between the class labels for the first time and we have proposed a method to extract these relations for a multi-label classification task and consequently, to use them in order to improve the predictions made by a multi-label classifier. We have conducted extensive experiments to assess the effectiveness of the proposed method. The obtained results advocate the merits of the proposed method in improving the multi-label classification results.
