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1. VBLSH: Volume-balancing locality-sensitive hashing algorithm for K-nearest neighbors search.

Author

Zhang, Shi, Lai, Huixia, Chen, Weilin, Zhang, Lulu, Lin, Xinhong, and Xiao, Ruliang

Subjects
*K-nearest neighbor classification, *ALGORITHMS, *SWARM intelligence, *MACHINE learning, *DATA mining, *PROBLEM solving, *HYPERCUBES, *INFORMATION retrieval
Abstract

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Deep instance envelope network-based imbalance learning algorithm with multilayer fuzzy C-means clustering and minimum interlayer discrepancy.

Author

Li, Fan, Zhang, Xiaoheng, Wang, Pin, and Li, Yongming

Subjects
FUZZY algorithms, CLASSIFICATION algorithms, DATA mining, MACHINE learning, PROBLEM solving, ALGORITHMS
Abstract

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. First, the MlFCM is designed for the original minority class instances to obtain deep instances and increase the diversity of instances. Then, the MIDMD is proposed to avoid the generation of noisy instances and maintain the consistency of the interlayers of instances. Next, the multilayer FCM and minimum interlayer discrepancy mechanism are combined to construct a deep instance envelope network – the MlFC&IDMD. Finally, an imbalance learning algorithm is proposed based on the MlFC&IDMD. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods. • For the first time, a multilayer FCM (MlFCM) algorithm for classification is proposed to mine the instances for more information. • A minimum interlayer discrepancy mechanism (MIDMD) is proposed to make the distribution of instances before and after clustering consistent. • A deep instance envelope network (MlFC&IDMD) is constructed by combining the MlFCM and MIDMD. • An efficient strategy is proposed to determine the number of layers L of the deep instance envelope network. • A new imbalance learning algorithm is proposed based on the MlFC&IDMD. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
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