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Bin-Bin Jia

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5 papers

1 author row

AAAI Conference 2025 Conference Paper

Evolutionary Classifier Chain for Multi-Dimensional Classification

Yu-Yang Zhang
Bin-Bin Jia
Min-Ling Zhang

In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order optimization and feature selection (ECCO) is proposed. Specifically, this approach designs a meta-heuristic algorithm to optimize the chain order of multiple classifiers. Simultaneously, the approach selects dimension-specific feature combinations that are more conducive to class prediction of each dimension. These strategies enhance the class prediction capability of the constructed MDC model. Comparative experiments on 14 real datasets validate that ECCO outperforms 7 state-of-the-art MDC approaches.

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IJCAI Conference 2024 Conference Paper

Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features

Teng Huang
Bin-Bin Jia
Min-Ling Zhang

In multi-dimensional classification (MDC), each instance is associated with multiple class variables characterizing the semantics of objects from different dimensions. To consider the dependencies among class variables and the specific characteristics contained in different semantic dimensions, a novel deep MDC approach named PIST is proposed to jointly deal with the two issues via learning pairwise dimension-specific features. Specifically, PIST conducts pairwise grouping to model the dependencies between each pair of class variables, which are more reliable with limited training samples. For extracting pairwise dimension-specific features, PIST weights the feature embedding with a feature importance vector, which is learned via utilizing a global loss measurement based on intra-class and inter-class covariance. Final prediction w. r. t. each dimension is determined by combining the joint probabilities related to this dimension. Comparative studies with eleven real-world MDC data sets clearly validate the effectiveness of the proposed approach.

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IJCAI Conference 2023 Conference Paper

Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification

Teng Huang
Bin-Bin Jia
Min-Ling Zhang

In multi-dimensional classification (MDC), each training example is associated with multiple class variables from different class spaces. However, it is rather costly to collect labeled MDC examples which have to be annotated from several dimensions (class spaces). To reduce the labeling cost, we attempt to deal with the MDC problem under the semi-supervised learning setting. Accordingly, a novel MDC approach named PLAP is proposed to solve the resulting semi-supervised MDC problem. Overall, PLAP works under the label propagation framework to utilize unlabeled data. To further consider dependencies among class spaces, PLAP deals with each class space in a progressive manner, where the previous propagation results will be used to initialize the current propagation procedure and all processed class spaces and the current one will be regarded as an entirety. Experiments validate the effectiveness of the proposed approach.

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AAAI Conference 2020 Conference Paper

Maximum Margin Multi-Dimensional Classification

Bin-Bin Jia
Min-Ling Zhang

Multi-dimensional classiﬁcation (MDC) assumes heterogenous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. Due to the heterogeneity of class spaces, the major difﬁculty in designing marginbased MDC techniques lies in that the modeling outputs from different class spaces are not comparable to each other. In this paper, a ﬁrst attempt towards maximum margin multidimensional classiﬁcation is investigated. Following the onevs-one decomposition within each class space, the resulting models are optimized by leveraging classiﬁcation margin maximization on individual class variable and model relationship regularization across class variables. We derive convex formulation for the maximum margin MDC problem, which can be tackled with alternating optimization admitting QP or closed-form solution in either alternating step. Experimental studies over real-world MDC data sets clearly validate effectiveness of the proposed maximum margin MDC techniques.

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AAAI Conference 2019 Conference Paper

Multi-Dimensional Classification via kNN Feature Augmentation

Bin-Bin Jia
Min-Ling Zhang

Multi-dimensional classification (MDC) deals with the problem where one instance is associated with multiple class variables, each of which specifies its class membership w. r. t. one specific class space. Existing approaches learn from MDC examples by focusing on modeling dependencies among class variables, while the potential usefulness of manipulating feature space hasn’t been investigated. In this paper, a first attempt towards feature manipulation for MDC is proposed which enriches the original feature space with kNNaugmented features. Specifically, simple counting statistics on the class membership of neighboring MDC examples are used to generate augmented feature vector. In this way, discriminative information from class space is encoded into the feature space to help train the multi-dimensional classification model. To validate the effectiveness of the proposed feature augmentation techniques, extensive experiments over eleven benchmark data sets as well as four state-of-the-art MDC approaches are conducted. Experimental results clearly show that, compared to the original feature space, classification performance of existing MDC approaches can be significantly improved by incorporating kNN-augmented features.

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