18,316 research outputs found
Cluster-based semi-supervised ensemble learning
Semi-supervised classification consists of acquiring knowledge from both labelled and unlabelled data to classify test instances. The cluster assumption represents one of the potential relationships between true classes and data distribution that semi-supervised algorithms assume in order to use unlabelled data. Ensemble algorithms have been widely and successfully employed in both supervised and semi-supervised contexts. In this Thesis, we focus on the cluster assumption to study ensemble learning based on a new cluster regularisation technique for multi-class semi-supervised classification. Firstly, we introduce a multi-class cluster-based classifier, the Cluster-based Regularisation (Cluster- Reg) algorithm. ClusterReg employs a new regularisation mechanism based on posterior probabilities generated by a clustering algorithm in order to avoid generating decision boundaries that traverses high-density regions. Such a method possesses robustness to overlapping classes and to scarce labelled instances on uncertain and low-density regions, when data follows the cluster assumption. Secondly, we propose a robust multi-class boosting technique, Cluster-based Boosting (CBoost), which implements the proposed cluster regularisation for ensemble learning and uses ClusterReg as base learner. CBoost is able to overcome possible incorrect pseudo-labels and produces better generalisation than existing classifiers. And, finally, since there are often datasets with a large number of unlabelled instances, we propose the Efficient Cluster-based Boosting (ECB) for large multi-class datasets. ECB extends CBoost and has lower time and memory complexities than state-of-the-art algorithms. Such a method employs a sampling procedure to reduce the training set of base learners, an efficient clustering algorithm, and an approximation technique for nearest neighbours to avoid the computation of pairwise distance matrix. Hence, ECB enables semi-supervised classification for large-scale datasets
EC3: Combining Clustering and Classification for Ensemble Learning
Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.Comment: 14 pages, 7 figures, 11 table
Online learning and detection of faces with low human supervision
The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses Âż Exemplar-based Nearest Neighbors (ÂżENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ÂżENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%).
As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft
A Convex Relaxation for Weakly Supervised Classifiers
This paper introduces a general multi-class approach to weakly supervised
classification. Inferring the labels and learning the parameters of the model
is usually done jointly through a block-coordinate descent algorithm such as
expectation-maximization (EM), which may lead to local minima. To avoid this
problem, we propose a cost function based on a convex relaxation of the
soft-max loss. We then propose an algorithm specifically designed to
efficiently solve the corresponding semidefinite program (SDP). Empirically,
our method compares favorably to standard ones on different datasets for
multiple instance learning and semi-supervised learning as well as on
clustering tasks.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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