Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting – as used by most ensemble based nonstationary learning algorithms – a strategic weighting mechanism is employed that tracks the classifiers’ performances over drifting environments to deter...
This paper introduces an adaptive framework that makes use of ensemble classification and self-train...
This paper presents a new ensemble method for learning from non-stationary data streams. In these si...
This paper focuses on the prevalent stage interference and stage performance imbalance of incrementa...
Incremental Learning on non stationary distribution has been shown to be a very challenging problem ...
Abstract—Many traditional supervised machine learning ap-proaches, either on-line or batch based, as...
The performance of the machine learning model always decreases with the occurrence of concept drift ...
Data stream classification becomes a promising prediction work with relevance to many practical envi...
Nowadays, many sources generate unbounded data streams at high incoming rates. It is impossible to s...
In recent years, the prevalence of technological advances has led to an enormous and ever-increasing...
Proceeding of: 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, 20-23 June 2013Learning...
Abstract—Learning in nonstationary environments, also known as learning concept drift, is concerned ...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Abstract. This paper proposes a boosting-like method to train a classifier ensemble from data stream...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
This paper introduces an adaptive framework that makes use of ensemble classification and self-train...
This paper introduces an adaptive framework that makes use of ensemble classification and self-train...
This paper presents a new ensemble method for learning from non-stationary data streams. In these si...
This paper focuses on the prevalent stage interference and stage performance imbalance of incrementa...
Incremental Learning on non stationary distribution has been shown to be a very challenging problem ...
Abstract—Many traditional supervised machine learning ap-proaches, either on-line or batch based, as...
The performance of the machine learning model always decreases with the occurrence of concept drift ...
Data stream classification becomes a promising prediction work with relevance to many practical envi...
Nowadays, many sources generate unbounded data streams at high incoming rates. It is impossible to s...
In recent years, the prevalence of technological advances has led to an enormous and ever-increasing...
Proceeding of: 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, 20-23 June 2013Learning...
Abstract—Learning in nonstationary environments, also known as learning concept drift, is concerned ...
Data stream classification is the process of learning supervised models from continuous labelled exa...
Abstract. This paper proposes a boosting-like method to train a classifier ensemble from data stream...
In this paper, we propose an incremental ensemble classifier learning method. In the proposed method...
This paper introduces an adaptive framework that makes use of ensemble classification and self-train...
This paper introduces an adaptive framework that makes use of ensemble classification and self-train...
This paper presents a new ensemble method for learning from non-stationary data streams. In these si...
This paper focuses on the prevalent stage interference and stage performance imbalance of incrementa...