Using search engines to retrieve information has become an important part of people's daily lives. For most search engines, click information is an important factor in document ranking. As a result, some websites cheat to obtain a higher rank by fraudulently increasing clicks to their pages, which is referred to as "Click Spam". Based on an analysis of the features of fraudulent clicks, a novel automatic click spam detection approach is proposed in this paper, which consists of 1. modeling user sessions with a triple sequence, which, to the best of our knowledge, takes into account not only the user action but also the action objective and the time interval between actions for the first time; 2. using the user-session bipartite graph propagation algorithm to take advantage of cheating users to find more cheating sessions; and 3. using the pattern-session bipartite graph propagation algorithm to obtain cheating session patterns to achieve higher precision and recall of click spam detection. Experimental results based on a Chinese commercial search engine using real-world log data containing approximately 80 million user clicks per day show that 2.6% of all clicks were detected as spam with a precision of up to 97%.

The accuracy of segmenting Chinese character, especially connected Chinese characters, is essential for the performance of a Chinese character recognition system. In this paper, a new approach for segmenting connected Chinese characters based on genetic algorithm is proposed. The best segmentation path is evolved by genetic algorithm from a fixed area located in the middle of character image which is defined as segmentation path zone (SPZ). The initial population is composed of each point line in SPZ. The individual coding, fitness function, crossover operator and mutation operator are also defined for this task. Experimental results on a dataset extracted from the four vaults show that our approach can get an average accuracy of 88.9% on test set and can handle some complex types of connected Chinese characters without special heuristic rules.

For numeral recognition, when a single classifier cannot provide a decision which is 100 percent correct, multiple classifier should be able to achieve higher accuracy. This is because group decisions are generally better than any individual's. In this paper, as evidence, the differences between a ANN classifier and a traditional classifier are discussed. Based on this concept combination methods were developed, which can aggregate the decisions obtained from individual, derive the best final decisions. But different combination methods lead to different performance: accuracy, efficiency and so on. A ANN combining algorithm is developed. The authors analyze it and a voting algorithm within experiments. First experiments on 10000 samples of handwritten numerals have powerfully shown that the results of two different individual classifiers with same features are disparate in performance. Second experiment have discussed two disparate combination methods in contrast.