Mining Contrast Subspaces

Duan, Lei; Tang, Guanting; Pei, Jian; Bailey, James E.; Dong, Guozhu; Campbell, Akiko; Tang, Changjie

doi:10.1007/978-3-319-06608-0_21

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…We tackled the problem of contrast subspace mining in [11], a preliminary version of this paper. Compared to that work, in this paper, we present a complete complexity analysis, provide a more detailed description of the key steps in our method and perform more extensive empirical evaluations, including using different bandwidths and kernel.…”

Section: Related Workmentioning

confidence: 99%

Efficient discovery of contrast subspaces for object explanation and characterization

et al. 2015

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We tackle the novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes C + and C − and a query object o, we want to find the top-k subspaces that maximize the ratio of likelihood of o in C + against that in C − . Such subspaces are very useful for characterizing an object and explaining how it differs between two classes. We demonstrate that this problem has important applications, and, at the same time, is very challenging, being MAX SNP-hard. We present CSMiner, a mining method that uses kernel density estimation in conjunction with various pruning techniques. B Lei DuanWe experimentally investigate the performance of CSMiner on a range of data sets, evaluating its efficiency, effectiveness, and stability and demonstrating it is substantially faster than a baseline method.

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Section: Related Workmentioning

confidence: 99%

Efficient discovery of contrast subspaces for object explanation and characterization

et al. 2015

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“…Emerging patterns are well recognized and effective in classifying highdimensional data, since an emerging pattern can handle a subpopulation in a subspace that deliberates a clear discriminative pattern Duan et al 2014]. However, it is still challenging to extend emerging patterns to classify datasets with streaming features.…”

Section: Introductionmentioning

confidence: 99%

Classification with Streaming Features: An Emerging-Pattern Mining Approach

Ding

Simovici

et al. 2015

ACM Trans. Knowl. Discov. Data

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Many datasets from real-world applications have very high-dimensional or increasing feature space. It is a new research problem to learn and maintain a classifier to deal with very high dimensionality or streaming features. In this article, we adapt the well-known emerging-pattern-based classification models and propose a semi-streaming approach. For streaming features, it is computationally expensive or even prohibitive to mine long-emerging patterns, and it is nontrivial to integrate emerging-pattern mining with feature selection. We present an online feature selection step, which is capable of selecting and maintaining a pool of effective features from a feature stream. Then, in our offline step, separated from the online step, we periodically compute and update emerging patterns from the pool of selected features from the online step. We evaluate the effectiveness and efficiency of the proposed method using a series of benchmark datasets and a real-world case study on Mars crater detection. Our proposed method yields classification performance comparable to the state-of-art static classification methods. Most important, the proposed method is significantly faster and can efficiently handle datasets with streaming features.

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“…Since it is not practical to manually examine a large number of features represents most of the real world data sets, mining contrast subspace has emerged to automate the process of discovering such abovementioned subspaces of an object. Given a multidimensional data set of two classes, a query object and a target class, mining contrast subspace finds subspaces where the query object is most similar to the target class while most dissimilar from other class [7], [8]. Those subspaces are also termed as contrast subspaces in the literature and it will be used throughout this paper.…”

Section: Introductionmentioning

confidence: 99%

Tree-based mining contrast subspace

Sia

Alfred

2019

Int. J. Adv. Intell. Informatics

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All existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bounds to identify inaccurate contrast subspaces for the given query object. This paper proposes a novel contrast subspace mining method that employs tree-based likelihood contrast scoring function which is not affected by the dimensionality of subspaces. The tree-based scoring measure recursively binary partitions the subspace space in the way that objects belong to the target class are grouped together and separated from objects belonging to other class. In contrast subspace, the query object should be in a group having a higher number of objects of the target class than other class. It incorporates the feature selection approach to find a subset of one-dimensional subspaces with high likelihood contrast score with respect to the query object. Therefore, the contrast subspaces are then searched through the selected subset of one-dimensional subspaces. An experiment is conducted to evaluate the effectiveness of the tree-based method in terms of classification accuracy. The experiment results show that the proposed method has higher classification accuracy and outperform the existing method on several real-world data sets.

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Mining Contrast Subspaces

Cited by 11 publications

References 17 publications

Efficient discovery of contrast subspaces for object explanation and characterization

Efficient discovery of contrast subspaces for object explanation and characterization

Classification with Streaming Features: An Emerging-Pattern Mining Approach

Tree-based mining contrast subspace

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