Search

Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in machine learning. Optimal scale combination (OSC) selection and attribute reduction are two key issues related to knowledge discovery in MDSs. However, searching for all OSCs may result in a combinatorial explosion, and the existing approaches typically incur excessive time consumption. In this study, searching for all OSCs is considered as an optimization problem with the scale space as the search space. Accordingly, a sequential three-way decision model of the scale space is established to reduce the search space by integrating three-way decision with the Hasse diagram. First, a novel scale combination is proposed to perform scale selection and attribute reduction simultaneously, and then an extended stepwise optimal scale selection (ESOSS) method is introduced to quickly search for a single local OSC on a subset of the scale space. Second, based on the obtained local OSCs, a sequential three-way decision model of the scale space is established to divide the search space into three pair-wise disjoint regions, namely the positive, negative, and boundary regions. The boundary region is regarded as a new search space, and it can be proved that a local OSC on the boundary region is also a global OSC. Therefore, all OSCs of a given MDS can be obtained by searching for the local OSCs on the boundary regions in a step-by-step manner. Finally, according to the properties of the Hasse diagram, a formula for calculating the maximal elements of a given boundary region is provided to alleviate space complexity. Accordingly, an efficient OSC selection algorithm is proposed to improve the efficiency of searching for all OSCs by reducing the search space. The experimental results demonstrate that the proposed method can significantly reduce computational time. [ABSTRACT FROM AUTHOR]

Naive Bayes classifier (NBC) is a classical binary generative classifier that has been extensively researched and developed for use in various applications owing to its simplicity and high efficiency. However, in practice, the distinct advantages of the NBC are often challenged by the conditional independence assumption among attributes and the zero-count problem. Moreover, the NBC strictly assigns a certain label to every object and lacks a classification mechanism to handle boundary objects; this may deteriorate the classification performances. Compared with binary classifiers, three-way classifiers provide a delayed decision for boundary objects. However, most existing three-way classifiers have been developed based on rough sets, which have high time complexity and decision conflict. In this study, based on the advantages of the NBC and three-way classifier, a novel three-way generative classifier with a weighted scoring distribution (3WGC-WSD) is proposed to improve the classification performances. First, to calculate the scores of an object under different classes, a scoring function that makes the best use of the advantages of parameter estimation in the NBC is defined. Second, a self-adaptive attribute weighted algorithm is designed to relax the attribute conditional independence assumption by attribute weighted and attribute reduction. Third, a non-parametric binary generative classifier with a weighted scoring function (2GC-WSF) is designed based on the scoring function and attribute weighted algorithm. Finally, inspired by the three-way decision, 3WGC-WSD is extended on 2GC-WSF to improve classification performances by providing delay decision for boundary objects. Experiments and comparisons on 15 widely-used UCI benchmark datasets demonstrate that 3WGC-WSD outperforms three state-of-the-art classifiers and three classical classifiers in terms of four indexes. Furthermore, the efficiency of 3WGC-WSD and 2GC-WSF is demonstrated in comparison with three classifiers on 10 datasets. [ABSTRACT FROM AUTHOR]

Recommender systems are an effective tool to resolve information overload by enabling the selection of the subsets of items from a universal set based on user preferences. The operation of most of recommender systems depends on the prediction ratings, which may introduce a degree of uncertainty into the process of recommendation. However, systems equipped with only two strategies lack the flexibility to address such uncertain decision-making problems. Thus, the presence of far-fetched recommendations accompanied by uncertainties often decreases recommendation quality. To resolve this issue, a three-way recommendation model based on a novel shadowed set is proposed in this paper to reduce decision-making risk and improve quality. To this end, a neighborhood rough set model is first introduced into three-way recommendation to determine similar user to active users with respect to the original rating decision system. This helps to avoid the uncertainty generated during the assignment of prediction rating. Subsequently, the optimal neighborhood radius is defined to overcome the subjectivity associated with the construction of the aforementioned neighborhood with a subjective parameter. Following this, a novel shadowed set model, based on neighborhood memberships of boundary users, is proposed to partition all users into positive region, negativity region and boundary region. This facilitates the adoption of different decisions by recommender systems for users in different regions. Finally, the effectiveness and reliability of the proposed model are verified on two Movielens datasets via comparison analyses. [ABSTRACT FROM AUTHOR]