At present, the scale of interval type data is getting larger and larger. When using the classic attribute reduction method to process, the data needs to be preprocessed,thus leading to the loss of original information. To solve this problem, a β-distribution reduction algorithm of the interval-valued decision system was proposed. Firstly, the concept and the reduction target of β-distribution of the interval-valued decision system were given, and the proposed related theories were proved. Then, the discernibility matrix and discernibility function of β-distribution reduction were constructed for the above reduction target,and the β-distribution reduction algorithm of the interval-valued decision system was proposed. Finally,14 UCI datasets were selected for experimental verification. On Statlog dataset, when the similarity threshold is 0.6 and the number of objects is 100, 200, 400, 600 and 846 respectively, the average reduction length of the β-distribution reduction algorithm is 1.6, 2.2, 1.4, 2.4 and 2.6 respectively, the average reduction length of the Distribution Reduction Algorithm based on Discernibility Matrix(DRADM) is 2.0, 3.0, 3.0, 4.0 and 4.0 respectively, the average reduction length of the Maximum Distribution Reduction Algorithm based on Discernibility Matrix(MDRADM) is 2.0, 3.0, 3.0, 4.0 and 3.0 respectively. The effectiveness of the proposed β-distribution reduction algorithm is verified by experimental results.

For urban transportation path planning issue, the speed of planning and the safety of vehicles in the path needed to be considered, but most existing reinforcement learning algorithms cannot consider both of them. Aiming at this problem, the following steps were carried out. First, a Dyna framework with the combination of model-based and model-independent algorithms was proposed, so as to improve the speed of planning. Then, the classical Sarsa algorithm was used as a route selection strategy in order to improve the safety of the algorithm. Finally, the above two were combined and an improved Sarsa-based algorithm called Dyna-Sa was proposed. Experimental results show that the reinforcement learning algorithm converges faster with more planning steps in advance. Compared with Q-learning, Sarsa and Dyna-Q algorithms through metrics such as convergence speed and number of collisions, it can be seen that the Dyna-Sa algorithm not only reduces the number of collisions in the map with obstacles, ensures the safety of vehicles in the urban traffic environment, but also accelerates the algorithm convergence.

There are a large number of dynamically-increasing interval data in practical applications. If the classic non-incremental attribute reduction of positive region is used for reduction, it is necessary to recalculate the positive region reduction of the updated interval-valued datasets, which greatly reduces the computational efficiency of attribute reduction. In order to solve the problem, incremental attribute reduction methods of positive region in interval-valued decision tables were proposed. Firstly, the related concepts of positive region reduction in interval-valued decision tables were defined. Then, the single and group incremental mechanisms of positive region were discussed and proved, and the single and group incremental attribute reduction algorithms of positive region in interval-valued decision tables were proposed. Finally, 8 UCI datasets were used to carry out experiments. When the incremental size of 8 datasets increases from 60% to 100%, the reduction time of classic non-incremental attribute reduction algorithm in the 8 datasets is 36.59 s, 72.35 s, 69.83 s, 154.29 s, 80.66 s, 1498.11 s, 4124.14 s and 809.65 s, the reduction time of single incremental attribute reduction algorithm is 19.05 s, 46.54 s, 26.98 s, 26.12 s, 34.02 s, 1270.87 s, 1598.78 s and 408.65 s, the reduction time of group incremental attribute reduction algorithm is 6.39 s, 15.66 s, 3.44 s, 15.06 s, 8.02 s, 167.12 s, 180.88 s and 61.04 s. Experimental results show that the proposed incremental attribute reduction algorithm of positive region in interval-valued decision tables is efficient.

The existing attribute reduction algorithms mostly focus on all decision classes in decision systems, but in actual decision process, decision makers may only focus on one or several decision classes in the decision systems. To solve this problem, a theoretical framework of positive region preservation reduction based on multi-specific decision classes in incomplete decision systems was proposed. Firstly, the positive region preservation reduction for single specific decision class in incomplete decision systems was defined. Secondly, the positive region preservation reduction for single specific decision class was extended to multi-specific decision classes, and the corresponding discernibility matrix and function were constructed. Thirdly, with related theorems analyzed and proved, an algorithm of Positive region preservation Reduction for Multi-specific decision classes reduction based on Discernibility Matrix in incomplete decision systems (PRMDM) was proposed. Finally, four UCI datasets were selected for experiments. On Teaching-assistant-evaluation, House, Connectionist-bench and Cardiotocography dataset, the average reduction length of Positive region preservation Reduction based on Discernibility Matrix in incomplete decision systems (PRDM) algorithm is 4.00, 13.00, 9.00 and 20.00 respectively while that of the PRMDM algorithm (with decision classes in the multi-specific decision classes is 2) is 3.00, 8.00, 8.00 and 18.00 respectively. The validity of PRMDM algorithm is verified by experimental results.

In classical heuristic attribute reduction algorithm for positive region, the attribute with the maximum dependency degree of the current positive domain should be added into the selected feature attribute subset in each iteration, leading to the large number of iterations and the low efficiency of the algorithm, and making the algorithm hard to be applied in the feature selection of high-dimensional and large-scale datasets. In order to solve the problems, the monotonic relationship between the positive regions in a decision system was studied and the formal description for the Multi-Scale Attribute Granule (MSAG) was given, and a Multi-scale Attribute Granule based Quick Positive Region reduction algorithm (MAG-QPR) was proposed. Each MSAG contains several attributes and can provide a large positive region for the selected feature attribute subset. As a result, adding MSAG in each iteration can reduce the number of the iteration and make the selected feature attribute subset more quickly approach to the positive region resolving ability of the condition attribute universal set. Therefore, the computational efficiency of the heuristic attribute reduction algorithm for positive region is improved. With 8 UCI datasets used for experiments, on the datasets Lung Cancer, Flag and German, the running time acceleration ratios of MAG-QPR to the general improved Feature Selection algorithm based on the Positive Approximation-Positive Region (FSPA-PR), the general improved Feature Selection algorithm based on the Positive Approximation-Shannon's Conditional Entropy (FSPA-SCE), the Backward Greedy Reduction Algorithm for positive region Preservation (BGRAP) and the Backward Greedy Reduction Algorithm for Generalized decision preservation (BGRAG) are 9.64, 15.70, 5.03, 2.50; 3.93, 7.55, 1.69, 4.57; and 3.61, 6.49, 1.30, 9.51 respectively. The experimental results show that, the proposed algorithm MAG-QPR can improve the algorithm efficiency and has better classification accuracy.

In big data environment, the research on mobile crowdsourcing system has become a research hotspot in Mobile Social Network (MSN). However, the selfishness of individuals in networks may cause the distrust problem of mobile crowdsourcing system. In order to inspire individuals to select trustful strategy, an online incentive mechanism based on reputation for mobile crowdsourcing system named RMI was proposed. Combining evolutionary game theory and Wright-Fisher model in biology, the evolution trend of mobile crowdsourcing system was studied. To solve free-riding and false-reporting problems, the reputation updating methods were established. Based on the above researches, an online incentive mechanism was built, which can inspire workers and requesters to select trustful strategies. The simulation results verify the effectiveness and adaptability of the proposed incentive mechanism. Compared with the traditional social norm-based reputation updating method, RMI can improve the trust degree of mobile crowdsourcing system effectively.

The existed trust models have two shortages in searching the trust path:firstly, factors affecting the trust value were not considered fully in the searching, or considered the same. Meanwhile, many algorithms ignored the importance of the interaction number when searching the trust path. In view of these problems, a searching algorithm of trust path based on graph theory was proposed. The concept of probability of honesty was put forward to weigh the credibility of the node further, and as the searching priority basis, it is more reasonable in the priority searching. Meanwhile it searched by filtering and used probability of multi-factors which affect the credibility of the node. The analyses of algorithm show that the complexity of the proposed algorithm is (n-m)² magnitude, much lower than the original fine-grained algorithm which complexity is n² magnitude. The experimental results show that the proposed algorithm can better filter out malicious nodes, improve the accuracy of the trust path search algorithms, and resist the attacks of malicious nodes.

The data sparseness is due to the nature of traditional collaborative filtering and trust-based recommender systems can effectively deal with the sparse data without losing accuracy. It is appropriate to use different methods for different users to give more personalized recommendation. The vertex characteristic in microcosmic stratums was studied, and the formal definition of interest was proposed. It was used to demonstrate the impact of local structures of the recommended user on the results of recommender systems. In the end, several results were given to illustrate the diversity of the effects of recommender systems on users of different types.