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Non-redundant and statistically significant discriminative high utility pattern mining algorithm
Jun WU, Aijia OUYANG, Ya WANG
Journal of Computer Applications    2025, 45 (8): 2572-2581.   DOI: 10.11772/j.issn.1001-9081.2024071063
Abstract26)   HTML0)    PDF (1526KB)(93)       Save

Aiming at the problems of false positive patterns and redundant patterns in tasks of discriminative high utility pattern mining, a discriminative high utility pattern mining algorithm based on unlimited testing and independent growth rate technique — UTDHU (Unlimited Testing for Discriminative High Utility pattern mining) was designed. Firstly, the discriminative high utility patterns that meet utility and difference thresholds were mined from a target transaction set. Then, the redundant patterns were screened out by independent growth rates of patterns which were calculated by constructing a shared tree of prefix-items. Finally, the statistical significance measure p-value for each remaining pattern was calculated by the unlimited testing, and the false positive discriminative high utility patterns were filtered out according to the family wise error rates. Experimental results on four benchmark transaction sets and two synthetic transaction sets show that compared with Hamm, YBHU (Yekutieli-Benjamini resampling for High Utility pattern mining) and other algorithms, the proposed algorithm outputs the least in terms of the number of patterns, with more than 97.8% of tested patterns moved. In terms of mode quality, the proportions of false positive discriminative high utility patterns of the proposed algorithm are less than 5.2% and the classification accuracies of constructed features of the proposed algorithm are at least 1.5 percentage points higher than those of the compared algorithms. Additionally, in terms of running time, although the proposed algorithm is slower than Hamm algorithm, it is faster than the other three algorithms based on statistical significance testing. It can be seen that the proposed algorithm can effectively eliminate a certain number of false positive and redundant discriminative high-utility patterns, exhibits superior mining performance, and achieves higher operational efficiency.

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General text classification model combining attention and cropping mechanism
Yumeng CUI, Jingya WANG, Xiaowen LIU, Shangyi YAN, Zhizhong TAO
Journal of Computer Applications    2023, 43 (8): 2396-2405.   DOI: 10.11772/j.issn.1001-9081.2022071071
Abstract438)   HTML26)    PDF (1774KB)(178)       Save

Focused on the issue that current classification models are generally effective on texts of one length, and a large number of long and short texts occur in actual scenes in a mixed way, a General Long and Short Text Classification Model based on Hybrid Neural Network (GLSTCM-HNN) was proposed. Firstly, BERT (Bidirectional Encoder Representations from Transformers) was applied to encode texts dynamically. Then, convolution operations were used to extract local semantic information, and a Dual Channel ATTention mechanism (DCATT) was built to enhance key text regions. Meanwhile, Recurrent Neural Network (RNN) was utilized to capture global semantic information, and a Long Text Cropping Mechanism (LTCM) was established to filter critical texts. Finally, the extracted local and global features were fused and input into Softmax function to obtain the output category. In comparison experiments on four public datasets, compared with the baseline model (BERT-TextCNN) and the best performing comparison model BERT, GLSTCM-HNN has the F1 scores increased by up to 3.87 and 5.86 percentage points respectively. In two generality experiments on mixed texts, compared with the generality model — CNN-BiLSTM/BiGRU hybrid text classification model based on Attention (CBLGA) proposed by existing research, GLSTCM-HNN has the F1 scores increased by 6.63 and 37.22 percentage points respectively. Experimental results show that the proposed model can improve the accuracy of text classification task effectively, and has generality of classification on texts with different lengths from training data and on long and short mixed texts.

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Feature selection algorithm based on neighborhood rough set and monarch butterfly optimization
Lin SUN, Jing ZHAO, Jiucheng XU, Xinya WANG
Journal of Computer Applications    2022, 42 (5): 1355-1366.   DOI: 10.11772/j.issn.1001-9081.2021030497
Abstract412)   HTML9)    PDF (1375KB)(109)       Save

The classical Monarch Butterfly Optimization (MBO) algorithm cannot handle continuous data well, and the rough set model cannot sufficiently process large-scale, high-dimensional and complex data. To address these problems, a new feature selection algorithm based on Neighborhood Rough Set (NRS) and MBO was proposed. Firstly, local disturbance, group division strategy and MBO algorithm were combined, and a transmission mechanism was constructed to form a Binary MBO (BMBO) algorithm. Secondly, the mutation operator was introduced to enhance the exploration ability of this algorithm, and a BMBO based on Mutation operator (BMBOM) algorithm was proposed. Then, a fitness function was developed based on the neighborhood dependence degree in NRS, and the fitness values of the initialized feature subsets were evaluated and sorted. Finally, the BMBOM algorithm was used to search the optimal feature subset through continuous iterations, and a meta-heuristic feature selection algorithm was designed. The optimization performance of the BMBOM algorithm was evaluated on benchmark functions, and the classification performance of the proposed feature selection algorithm was evaluated on UCI datasets. Experimental results show that, the proposed BMBOM algorithm is significantly better than MBO and Particle Swarm Optimization (PSO) algorithms in terms of the optimal value, worst value, average value and standard deviation on five benchmark functions. Compared with the optimized feature selection algorithms based on rough set, the feature selection algorithms combining rough set and optimization algorithms, the feature selection algorithms combining NRS and optimization algorithms, the feature selection algorithms based on binary grey wolf optimization, the proposed feature selection algorithm performs well in the three indicators of classification accuracy, the number of selected features and fitness value on UCI datasets, and can select the optimal feature subset with few features and high classification accuracy.

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Ant colony optimization algorithm based on chaotic disturbance and neighborhood exchange for vehicle routing problem
LI Ya WANG Dong
Journal of Computer Applications    2012, 32 (02): 444-447.   DOI: 10.3724/SP.J.1087.2012.00444
Abstract1455)      PDF (656KB)(497)       Save
A new ant colony algorithm based on chaotic disturbance and neighborhood exchange was proposed to solve the Vehicle Routing Problem (VRP). Concerning the standard ant colony algorithm's shortcomings such as long search time, being prone to premature convergence, non-optimal solution and so on, the new algorithm used the randomness, ergodicity and regularity of chaos to adjust the pheromone of a small part of the routes with the chaotic disturbance strategy when the algorithm was getting into a prematurity. For the standard ant colony algorithm has the greedy rule with randomness, the new algorithm used the neighborhood exchange strategy to adjust the optimal solution. The simulation results show that the new algorithm is better than the standard ant colony algorithm and genetic algorithm when solving the VRPs of different sizes.
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Routing selection algorithm under multiple link state limited
Weiya Wang
Journal of Computer Applications   
Abstract2188)      PDF (560KB)(1604)       Save
The Combination of Genetic Algorithm and Ant Colony Algorithm inherits the advantages of Genetic Algorithm and Ant Colony Algorithm, and has a higher efficiency than Genetic Algorithm and a faster speed than Ant Colony Algorithm in computing the shortest route under multi link state limited. The testing proves that the Combination Algorithm is a new better heuristic algorithm both in the efficiency of precise results and time.
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