3,432 research outputs found
Algorithm selection on data streams
We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Combining similarity in time and space for training set formation under concept drift
Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications
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