20,858 research outputs found
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Class imbalance classification is a challenging research problem in data
mining and machine learning, as most of the real-life datasets are often
imbalanced in nature. Existing learning algorithms maximise the classification
accuracy by correctly classifying the majority class, but misclassify the
minority class. However, the minority class instances are representing the
concept with greater interest than the majority class instances in real-life
applications. Recently, several techniques based on sampling methods
(under-sampling of the majority class and over-sampling the minority class),
cost-sensitive learning methods, and ensemble learning have been used in the
literature for classifying imbalanced datasets. In this paper, we introduce a
new clustering-based under-sampling approach with boosting (AdaBoost)
algorithm, called CUSBoost, for effective imbalanced classification. The
proposed algorithm provides an alternative to RUSBoost (random under-sampling
with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost)
algorithms. We evaluated the performance of CUSBoost algorithm with the
state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,
SMOTEBoost on 13 imbalance binary and multi-class datasets with various
imbalance ratios. The experimental results show that the CUSBoost is a
promising and effective approach for dealing with highly imbalanced datasets.Comment: CSITSS-201
Combination of Multiple Bipartite Ranking for Web Content Quality Evaluation
Web content quality estimation is crucial to various web content processing
applications. Our previous work applied Bagging + C4.5 to achive the best
results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of
many point-wise rankinig models. In this paper, we combine multiple pair-wise
bipartite ranking learner to solve the multi-partite ranking problems for the
web quality estimation. In encoding stage, we present the ternary encoding and
the binary coding extending each rank value to (L is the number of the
different ranking value). For the decoding, we discuss the combination of
multiple ranking results from multiple bipartite ranking models with the
predefined weighting and the adaptive weighting. The experiments on ECML/PKDD
2010 Discovery Challenge datasets show that \textit{binary coding} +
\textit{predefined weighting} yields the highest performance in all four
combinations and furthermore it is better than the best results reported in
ECML/PKDD 2010 Discovery Challenge competition.Comment: 17 pages, 8 figures, 2 table
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201
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