Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Local Averaging of Ensembles of LVQ-Based Nearest Neighbor Classifiers
Applied Intelligence
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Mining Concept Drifts from Data Streams Based on Multi-Classifiers
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution
IEEE Transactions on Knowledge and Data Engineering
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
IEEE Internet Computing
A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Classification Using Streaming Random Forests
IEEE Transactions on Knowledge and Data Engineering
Building a highly-compact and accurate associative classifier
Applied Intelligence
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IEEE Transactions on Knowledge and Data Engineering
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
Rough sets for adapting wavelet neural networks as a new classifier system
Applied Intelligence
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Stream mining on univariate uncertain data
Applied Intelligence
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
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Due to concept drifts, maintaining an up-to-date model is a challenging task for most of the current classification approaches used in data stream mining. Both the incremental classifiers and the ensemble classifiers spend most of their time in updating their temporary models and at the same time, a big sample buffer for training a classifier is necessary for most of them. These two drawbacks constrain further application in classifying a data stream. In this paper, we present a hormone based nearest neighbor classification algorithm for data stream classification, in which the classifier is updated every time a new record arrives. The records could be seen as locations in the feature space, and each location can accommodate only one endocrine cell. The classifier consists of endocrine cells on the boundaries of different classes. Every time a new record arrives, the cell that resides in the most unfit location will move to the new arrived record. In this way, the changing boundaries between different classes are recorded by the locations where endocrine cells reside in. The main advantages of the proposed method are the saving of the sample buffer and the improving of the classification accuracy. It is very important for conditions where the hardware resources are very expensive or the main memory is limited. Experiments on synthetic and real life data sets show that the proposed algorithm is able to classify data streams with less memory space and classification error.