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Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Title Advances in knowledge discovery and data mining : 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings. Part I / Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi (eds.).
Imprint Cham, Switzerland : Springer, 2018.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Series Lecture notes in computer science, 0302-9743 ; 10937.
Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Lecture notes in computer science ; 10937. 0302-9743.
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Alt Name Phung, Dinh,
Tseng, Vincent S.,
Webb, Geoffrey I.,
Ho, Bao,
Ganji, Mohadeseh,
Rashidi, Lida,
Add Title PAKDD 2018
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Series Lecture notes in computer science, 0302-9743 ; 10937.
Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Lecture notes in computer science ; 10937. 0302-9743.
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Alt Name Phung, Dinh,
Tseng, Vincent S.,
Webb, Geoffrey I.,
Ho, Bao,
Ganji, Mohadeseh,
Rashidi, Lida,
Add Title PAKDD 2018
Description 1 online resource : illustrations.
Note Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed July 2, 2018).
Summary This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018. The 164 full papers were carefully reviewed and selected from 592 submissions. The volumes present papers focusing on new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.
Contents Intro -- PC Chairs' Preface -- General Chairs' Preface -- Organization -- Contents -- Part I -- Classification and Supervised Machine Learning -- Classifier Risk Estimation Under Limited Labeling Resources -- 1 Introduction -- 2 Problem Formulation -- 3 Estimation Methods -- 3.1 Simple Random Sampling -- 3.2 Stratified Sampling -- 3.3 Allocation Methods for Stratified Sampling -- 3.4 Comparison of Variances -- 3.5 Stratification Methods -- 4 Experiments and Results -- 4.1 Proportional and Equal Allocation -- 4.2 Optimal Allocation -- 4.3 Dependence on True Accuracy
5 Discussions and Conclusions -- References -- Social Stream Classification with Emerging New Labels -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 The Proposed Framework -- 4.1 NL-Forest: Training Process -- 4.2 NL-Forest: Deployment -- 4.3 NL-Forest: Model Update -- 4.4 Model Complexity -- 5 Experiment -- 5.1 Experimental Setup -- 5.2 Simulated Data Stream -- 5.3 Real Data Stream -- 5.4 Sensitivity of Parameters -- 6 Conclusion -- References -- Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules -- 1 Introduction -- 2 Preliminaries
2.1 Multi-label Rule Learning -- 2.2 Bipartition Evaluation Functions -- 3 Properties of Multi-label Evaluation Measures -- 4 Algorithm for Learning Multi-label Head Rules -- 5 Evaluation -- 6 Related Work -- 7 Conclusions -- References -- Modeling Label Interactions in Multi-label Classification: A Multi-structure SVM Perspective -- 1 Introduction -- 2 A Quick Review of Existing Work -- 3 Multi-structure SVM -- 4 Dual MSSVM and an Efficient Optimization Algorithm -- 5 Experiments -- 6 Conclusion -- References -- Sentiment Classification Using Neural Networks with Sentiment Centroids
1 Introduction -- 2 Related Work -- 2.1 Sentiment Features Learning -- 2.2 Neural Networks for Sentiment Classification -- 3 Our Approach -- 3.1 Text Sequence Encoder Models -- 3.2 Sentiment Centriods Constraint -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Training Settings -- 4.4 Sentence-Level Classification -- 4.5 Document-Level Classification -- 4.6 The Effect of Sentiment Centroids -- 5 Conclusion and Future Work -- References -- Random Pairwise Shapelets Forest -- 1 Introduction -- 2 Random Pairwise Shapelets Forest -- 2.1 Providing More Information by Combination
2.2 Proposed Algorithm -- 3 Decomposed Mean Decrease Impurity -- 4 Experiment and Evaluation -- 4.1 Experimental Setup -- 4.2 Predictive Performance -- 4.3 Computational Performance -- 5 Case Studies -- 5.1 GunPoint -- 5.2 ArrowHead -- 6 Conclusion -- References -- A Locally Adaptive Multi-Label k-Nearest Neighbor Algorithm -- 1 Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Paper Organization -- 2 Related Work -- 3 Methodology -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Results -- 5 Conclusion -- References -- Classification with Reject Option Using Conformal Prediction
ISBN 9783319930343 (electronic bk.)
3319930346 (electronic bk.)
3319930338
9783319930336
9783319930336 (print)
9783319930350 (print)
3319930354
ISBN/ISSN 10.1007/978-3-319-93034-3
OCLC # 1042329207
Additional Format Printed edition: 9783319930336.


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