A Fast Adaptive Classification Approach Using Kernel Ridge Regression and Clustering for Non-stationary Data Stream
| Autor: | Vedaanta Agarwalla, Chandan Gautam, Raman Bansal, Ruchir Garg, Aruna Tiwari |
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| Rok vydání: | 2018 |
| Předmět: |
Stationary process
Concept drift Computer science Active learning (machine learning) business.industry Small number 020208 electrical & electronic engineering Pattern recognition 02 engineering and technology 01 natural sciences 010309 optics Multiclass classification ComputingMethodologies_PATTERNRECOGNITION Kernel ridge regression Kernel (statistics) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Artificial intelligence Cluster analysis business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9789811309229 |
| Popis: | Classification on non-stationary data requires faster evolving of the model while keeping the accuracy levels consistent. We present here a faster and reliable model to handle non-stationary data when a small number of labelled samples are available with the stream of unlabelled samples. An active learning model is proposed with the help of supervised model, i.e. Kernel Ridge Regression (KRR) with the combination of an unsupervised model, i.e. K-means clustering to handle the concept drift in the data efficiently. Proposed model consumes less time and at the same time yields similar or better accuracy compared to the existing clustering-based active learning methods. |
| Databáze: | OpenAIRE |
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