By Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding, Moonis Ali, Xindong Wu (eds.)
This quantity constitutes the completely refereed convention complaints of the twenty fifth overseas convention on business Engineering and different purposes of utilized Intelligend structures, IEA/AIE 2012, held in Dalian, China, in June 2012. the full of eighty two papers chosen for the complaints have been conscientiously reviewed and chosen from a variety of submissions. The papers are prepared in topical sections on laptop studying equipment; cyber-physical procedure for clever transportation functions; AI purposes; evolutionary algorithms, combinatorial optimization; modeling and aid of cognitive and affective human techniques; typical language processing and its functions; social community and its purposes; mission-critical purposes and case experiences of clever structures; AI tools; sentiment research for asian languages; elements on cognitive computing and clever interplay; spatio-temporal datamining, established studying and their functions; determination making and data dependent platforms; trend acceptance; agent established platforms; choice making concepts and leading edge wisdom administration; computer studying applications.
Read or Download Advanced Research in Applied Artificial Intelligence: 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012. Proceedings PDF
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Additional info for Advanced Research in Applied Artificial Intelligence: 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012. Proceedings
The specific case of classifying products to a product catalog has been studied in . An improvement on Naïve Bayes for integrating documents from a source catalog to a master catalog is discussed. The idea is that, if two documents belong to the same category in the source then, because they must be similar, they may belong to the same category in the master catalog with a higher probability. The authors design a method to tune a coefficient value that represents the weight of this parameter in the classification calculations.
4 Product Classification In this work, there are two key features used for classification, namely product names and their prices. A product name can be considered as a bag (or possibly a set) of terms . Before the classification process, preprocessing is applied on these names: terms in the product names are extracted; word stemming is applied; stop words and punctuations are removed. Therefore, every product is represented with a list of terms extracted from their names. As mentioned earlier, product data of the master store is used as the training set.
This subset is found by using the confidence of the category selection. This method is further extended in Method III by including the order of the products in the product file extracted from the source store. The assumption is that the neighboring products have a higher chance to belong to the same category. We tested out implementation on the products extracted from 6 source stores. Remarkable improvements on F-scores and success rates are observed. This approach can further be improved in several directions.
Advanced Research in Applied Artificial Intelligence: 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012. Proceedings by Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding, Moonis Ali, Xindong Wu (eds.)