Titlebook: Machine Learning and Data Mining in Pattern Recognition; 14th International C Petra Perner Conference proceedings 2018 Springer Internation

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0302-9743 and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. .The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and

冥界三河

Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets,to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches.

丰满中国

A Tag2Vec Approach for Questions Tag Suggestion on Community Question Answering Sites,lete tagging. To overcome this issue, we design an automatic tag suggestion technique which can suggest tags to the users based on their question text. It serves to minimize the error of the manual tagging system by providing more relevant tags to questions. The performance of the proposed system is evaluated using Precision, Recall, and F1-score.

AGONY

Association Rule Mining in Fuzzy Political Donor Communities,n analyzed with association rule mining to find distinguishing features within the resulting communities. The results show the mined rules help identify notable features for the communities and aid in understanding both shared and differing community characteristics.

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Memory Efficient Frequent Itemset Mining, approach in which transactions are represented in a compact graph with the number of nodes equal to the number of distinct items in a database. Our experimental results confirm the efficiency of memory use without significantly sacrificing the execution time of the mining algorithm.

调整

Automatic Keyphrase Extraction Using Recurrent Neural Networks,traction algorithm using a siamese LSTM network, eliminating the need for manual feature engineering. We train and evaluate our model on the . [.] dataset for keyphrase extraction and achieve comparable results to state-of-the-art algorithms.