Highly Scalable Attribute Selection for Averaged One-Dependence Estimators

Chen, Shenglei; Martínez, Ana María; Webb, Geoffrey I.

doi:10.1007/978-3-319-06605-9_8

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…On the one hand, it is worthwhile to explore the technique of selecting both parents and children. On the other hand, we can also combine the fast attribute selection technique based on leave one out cross validation proposed in [4] to further improve the classification accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…At the same time, we want to obtain a thorough idea on how selective AnDE is compared with other improvements of AODE. As the study in [4] shows that ASAODE outperforms such improvements of AODE as weightily AODE, AODE with Subsumption Resolution and AODE with BSE, we add ASADOE here to give a thorough comparison.…”

Section: Selective Ande Compared To Ande and Asaodementioning

confidence: 98%

“…Chen et al [4] proposed a new attribute selection algorithm for AODE, referred to as ASAODE. This algorithm can search in a large model space, while it requires only a single extra pass through the training data, resulting in a computationally efficient two-pass learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Selective AnDE for large data learning: a low-bias memory constrained approach

et al. 2016

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Learning from data that are too big to fit into memory poses great challenges to currently available learning approaches. Averaged n-Dependence Estimators (AnDE) allows for a flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for learning from large quantities of data. Memory requirement in AnDE, however, increases combinatorially with the number of attributes and the parameter n. In large data learning, number of attributes is often large and we also expect high n to achieve low-bias classification. In order to achieve the lower bias of AnDE with higher n but with less memory requirement, we propose a memory constrained selective AnDE algorithm, in which two passes of learning through training examples are involved. The first pass performs attribute selection on super parents according to available memory, whereas the second one learns an AnDE model with parents only on the selected attributes. Extensive experiments show that the new selective AnDE has considerably lower bias and prediction error relative to An DE, where n = n − 1, while maintaining the same space complexity and similar time complexity. The proposed algorithm works well on categorical data. Numerical data sets need to be discretized first.

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Section: Resultsmentioning

confidence: 99%

Section: Selective Ande Compared To Ande and Asaodementioning

confidence: 98%

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Selective AnDE for large data learning: a low-bias memory constrained approach

et al. 2016

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“…Because the main purpose for this paper is to design a good weighting model for ensembling SPODEs, and our analysis on the 56 benchmark data sets has already demonstrated the performance of the proposed SODE, we use the text and image learning tasks to demonstrate the generality of the SODE for different applications. Moreover, the SPODE model (e.g., AODE) has already been improved for the highly scalable attribute problem in [10]. Also, SPODE models can be trained incrementally [5].…”

Section: Text Categorization Taskmentioning

confidence: 99%

SODE: Self-Adaptive One-Dependence Estimators for classification

Pan

Zhu

et al. 2016

Pattern Recognition

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SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute independence assumption of Naive Bayes (NB) to allow each attribute to depend on a common single attribute (superparent). SPODEs can effectively handle data with attribute dependency but still inherent NB's key advantages such as computational efficiency and robustness for high dimensional data. In reality, determining an optimal superparent for SPODEs is difficult. One common approach is to use weighted combinations of multiple SPODEs, each having a different superparent, by assigning a proper weight value to each superparent (i.e., an attribute). In this paper, we propose a self-adaptive SPODEs, namely SODE, which uses immunity theory in artificial immune systems to automatically and self-adaptively select the weight for each single SPODE. SODE does not need to know the importance of individual SPODE nor the relevance among SPODEs, so it can flexibly and efficiently search optimal weight values for each SPODE during the learning process. Extensive experiments and comparisons on 56 benchmark data sets, and validations on image retrieval and document categorization demonstrate that SODE is suitable for a wide range of tasks and outperforms other state-of-the-art weighted SPODE algorithms. Results also confirm that SODE provides an appropriate balance between runtime efficiency and accuracy effectiveness.

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“…• Attribute selection [16] performs attribute (parent or children or both) eliminations in each iteration to reduce zero-one loss.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators

et al. 2020

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Of numerous proposals for weakening the attribute independence assumption of Naive Bayes, averaged one-dependence estimators (AODE) learns by extrapolation from marginal to full-multivariate probability distributions, and has demonstrated reasonable improvement in terms of classification performance. However, all the one-dependence estimators in AODE are assigned with the same weight, and their probability estimates are combined linearly. This work presents an efficient and effective attribute value weighting approach that assigns discriminative weights to different super-parent one-dependence estimators for different instances by identifying the differences among these one-dependence estimators in terms of log likelihood. The proposed approach is validated on widely used benchmark datasets from UCI machine learning repository. Experimental results show that the proposed approach achieves bias-variance trade-off and is a competitive alternative to state-of-the-art Bayesian and non-Bayesian learners (e.g., tree augmented Naive Bayes and logistic regression).

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Highly Scalable Attribute Selection for Averaged One-Dependence Estimators

Cited by 12 publications

References 11 publications

Selective AnDE for large data learning: a low-bias memory constrained approach

Selective AnDE for large data learning: a low-bias memory constrained approach

SODE: Self-Adaptive One-Dependence Estimators for classification

Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators

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