The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Any time induction of decision trees: an iterative improvement approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Self-Adaptive Anytime Stream Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Naive bayes classifiers that perform well with continuous variables
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
BT*: an advanced algorithm for anytime classification
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
| Hi-index | 0.00 |
In scientific databases large amounts of data are collected to create knowledge repositories for deriving new insights or planning further experiments. These databases can be used to train classifiers that later categorize new data tuples. However, the large amounts of data might yield a time consuming classification process, e.g. for nearest neighbors or kernel density estimators. Anytime classifiers bypass this drawback by being interruptible at any time while the quality of the result improves with higher time allowances. Interruptible classifiers are especially useful when newly arriving data has to be classified on demand, e.g. during a running experiment. A statistical approach to anytime classification has recently been proposed using Bayes classification on kernel density estimates. In this paper we present a novel data structure called MC-Tree (Multi-Class Tree) that significantly improves Bayesian anytime classification. The tree stores a hierarchy of mixture densities that represent objects from several classes. Data transformations are used during tree construction to optimize the condition of the tree with respect to multiple classes. Anytime classification is achieved through novel query dependent model refinement approaches that take the entropy of the current mixture components into account. We show in experimental evaluation that the MC-Tree outperforms previous approaches in terms of anytime classification accuracy.