Many algorithms have been designed in the past that tackle the problem of concept drift in data s... more Many algorithms have been designed in the past that tackle the problem of concept drift in data streams. We present a new approach Weighted Novel Class Detection (WNCD), a diversified ensemble approach that combines the concepts of ensemble learning, instance weighting and diversity, for handling drifting concepts and detection of novel class instances. Based on the empirical results using various artificial and real time datasets, WNCD gives better performance as compared to the existing online approaches in handling concept drift and novelty detection.
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual... more Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the existing single classifier approaches and the online ensemble approaches. Empirical results would prove that our approach is highly competitive, giving good accuracy and speed in handling and identifying drifts in data, irrespective of noise present in the dataset.
AnADAS function developed within the DESERVE platform and the tuning of this function for a parti... more AnADAS function developed within the DESERVE platform and the tuning of this function for a particular application is discussed in this chapter. Based on separating the software and tuning data, according to the standards described in detail in Chapter 2, such a function can also be used for an alternate vehicle or application use case. The opportunities as well as the potential challenges are described, using a real world example, developed within the DESERVE Project.
Many algorithms have been designed in the past that tackle the problem of concept drift in data s... more Many algorithms have been designed in the past that tackle the problem of concept drift in data streams. We present a new approach Weighted Novel Class Detection (WNCD), a diversified ensemble approach that combines the concepts of ensemble learning, instance weighting and diversity, for handling drifting concepts and detection of novel class instances. Based on the empirical results using various artificial and real time datasets, WNCD gives better performance as compared to the existing online approaches in handling concept drift and novelty detection.
Many algorithms have been designed in the past that tackle the problem of concept drift in data s... more Many algorithms have been designed in the past that tackle the problem of concept drift in data streams. We present a new approach Weighted Novel Class Detection (WNCD), a diversified ensemble approach that combines the concepts of ensemble learning, instance weighting and diversity, for handling drifting concepts and detection of novel class instances. Based on the empirical results using various artificial and real time datasets, WNCD gives better performance as compared to the existing online approaches in handling concept drift and novelty detection.
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual... more Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the existing single classifier approaches and the online ensemble approaches. Empirical results would prove that our approach is highly competitive, giving good accuracy and speed in handling and identifying drifts in data, irrespective of noise present in the dataset.
AnADAS function developed within the DESERVE platform and the tuning of this function for a parti... more AnADAS function developed within the DESERVE platform and the tuning of this function for a particular application is discussed in this chapter. Based on separating the software and tuning data, according to the standards described in detail in Chapter 2, such a function can also be used for an alternate vehicle or application use case. The opportunities as well as the potential challenges are described, using a real world example, developed within the DESERVE Project.
Many algorithms have been designed in the past that tackle the problem of concept drift in data s... more Many algorithms have been designed in the past that tackle the problem of concept drift in data streams. We present a new approach Weighted Novel Class Detection (WNCD), a diversified ensemble approach that combines the concepts of ensemble learning, instance weighting and diversity, for handling drifting concepts and detection of novel class instances. Based on the empirical results using various artificial and real time datasets, WNCD gives better performance as compared to the existing online approaches in handling concept drift and novelty detection.
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Papers by Abhishek Ravi