Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions

Chen, Ke; Wang, Shihai

doi:10.1109/tpami.2010.92

Cited by 124 publications

(65 citation statements)

References 14 publications

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“…With SSC we may pursue two different objectives: transductive and inductive classification [5]. The former is devoted to predicting the correct labels of a set of unlabeled examples that is also used during the training phase.…”

Section: Introductionmentioning

confidence: 99%

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SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Triguero

García

Herrera

2015

IEEE Trans. Cybern.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-Self-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant proposals are currently inspired by boosting schemes to iteratively enlarge the labeled set. Despite their effectiveness, these methods are constrained by the number of labeled examples and their distribution, which in many cases is sparse and scattered. The aim of this work is to design a framework, named SEG-SSC, to improve the classification performance of any given self-labeled method by using synthetic labeled data. These are generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference. Next, these examples are incorporated in the main stages of the self-labeling process. The principal aspects of the proposed framework are: (a) introducing diversity to the multiple classifiers used by using more (new) labeled data, (b) fulfilling labeled data distribution with the aid of unlabeled data, and (c) being applicable to any kind of self-labeled method. In our empirical studies, we have applied this scheme to four recent self-labeled methods, testing their capabilities with a large number of data sets. We show that this framework significantly improves the classification capabilities of self-labeled techniques.

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Section: Introductionmentioning

confidence: 99%

“…As regards examples of models based on the cluster assumption, we can find generative models [9] or semi-supervised support vector machines [10]. Recent studies have addressed multiple assumptions in one model [11], [5], [12].…”

Section: Introductionmentioning

confidence: 99%

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Triguero

García

Herrera

2015

IEEE Trans. Cybern.

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“…SemiBoost combines supervised learning with semisupervised learning by utilizing both the labelled and unlabelled training data [18]. Its purpose is to reduce the generalization error when the labelled training data are insufficient [19]. FloatBoost deletes the less effective weak classifiers during the training by a backtracking mechanism so that it outperforms AdaBoost when it has the same number of weak classifiers as AdaBoost [20].…”

Section: Introductionmentioning

confidence: 99%

Penalized AdaBoost: Improving the Generalization Error of Gentle AdaBoost through a Margin Distribution

Nagahashi

2015

IEICE Trans. Inf. & Syst.

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SUMMARYGentle AdaBoost is widely used in object detection and pattern recognition due to its efficiency and stability. To focus on instances with small margins, Gentle AdaBoost assigns larger weights to these instances during the training. However, misclassification of small-margin instances can still occur, which will cause the weights of these instances to become larger and larger. Eventually, several large-weight instances might dominate the whole data distribution, encouraging Gentle AdaBoost to choose weak hypotheses that fit only these instances in the late training phase. This phenomenon, known as "classifier distortion", degrades the generalization error and can easily lead to overfitting since the deviation of all selected weak hypotheses is increased by the late-selected ones. To solve this problem, we propose a new variant which we call "Penalized AdaBoost". In each iteration, our approach not only penalizes the misclassification of instances with small margins but also restrains the weight increase for instances with minimal margins. Our method performs better than Gentle AdaBoost because it avoids the "classifier distortion" effectively. Experiments show that our method achieves far lower generalization errors and a similar training speed compared with Gentle AdaBoost.

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“…Some SSIL algorithms have been developed recently, such as RegBoost [22] and SemiBoost [23]. A more detailed review on semi-supervised learning can be found in [24].…”

Section: Introductionmentioning

confidence: 99%

“…Manifold assumes that the high-dimensional data lie on a low-dimensional nonlinear manifold. Properties of the manifold ensure more accurate density estimation or more appropriate similarity measures [22].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

Wang¹,

Almasri²,

Shi³

et al. 2014

CBIO

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Protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interfaces. Hot spots make a dominant contribution to the binding free energy and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there exists a need for accurate and reliable computational hot spot prediction methods. Compared to the supervised hot spot prediction algorithms, the semi-supervised prediction methods can take into consideration both the labeled and unlabeled residues in the dataset during the prediction procedure. The transductive support vector machine has been utilized for this task and demonstrated a better prediction performance. To the best of our knowledge, however, none of the transductive semi-supervised algorithms takes all the three semi-supervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue prediction, by considering all the three semi-supervised assumptions using nonlinear models. Our algorithm, IterPropMCS, works in an iterative manner. In each iteration, the algorithm first propagates the labels of the labeled residues to the unlabeled ones, along the shortest path between them on a graph, assuming that they lie on a nonlinear manifold. Then it selects the most confident residues as the labeled ones for the next iteration, according to the cluster and smoothness criteria, which is implemented by a nonlinear density estimator. Experiments on a benchmark dataset, using protein structure-based features, demonstrate that our approach is effective in predicting hot spots and compares favorably to other available methods. The results also show that our method outperforms the state-of-the-art transductive learning methods.

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Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions

Cited by 124 publications

References 14 publications

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Penalized AdaBoost: Improving the Generalization Error of Gentle AdaBoost through a Margin Distribution

Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

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