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Spark Rough Hypercuboid Approach for Scalable Feature Selection

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 3130-3144页

作者： Luo, Chuan Wang, Sizhao Li, Tianrui Chen, Hongmei Lv, Jiancheng Yi, Zhang Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Sichuan Peoples R China

Feature selection refers to choose an optimal non-redundant feature subset with minimal degradation of learning performance and maximal avoidance of data overfitting. The appearance of large data explosion leads to the sequential execution of algorithms are extremely time-consuming, which necessitates the scalable parallelization of algorithms by efficiently exploiting the distributed computational capabilities. In this paper, we present parallel feature selection algorithms underpinned by a rough hypercuboid approach in order to scale for the growing data volumes. Metrics in terms of rough hypercuboid are highly suitable to parallel distributed processing, and fits well with the Apache Spark cluster computing paradigm. Two data parallelism strategies, namely, vertical partitioning and horizontal partitioning, are implemented respectively to decompose the data into concurrent iterative computing streams. Experimental results on representative datasets show that our algorithms significantly faster than its original sequential counterpart while guaranteeing the quality of the results. Furthermore, the proposed algorithms are perfectly capable of exploiting the distributed-memory clusters to accomplish the computation task that fails on a single node due to the memory constraints. Parallel scalability and extensibility analysis have confirmed that our parallelization extends well to process massive amount of data and can scales well with the increase of computational nodes.

关键词： Feature extraction Rough sets partitioning algorithms Computational modeling Approximation algorithms Parallel processing Heuristic algorithms Rough hypercuboid feature selection parallel computing scalability apache spark

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QPA: A Quantization-Aware Piecewise Polynomial Approximation Methodology for Hardware-Efficient Implementations

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2023年第7期31卷 931-944页

作者： Geng, Haoran Chen, Xiaoliang Zhao, Ning Du, Yuan Du, Li Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China

Piecewise polynomial approximation (PPA) on nonlinear functions plays an important role in high-precision computing. In this article, we proposed QPA, an integration of error-flattened quantization-aware PPA methods, to generate the optimized coefficients for efficient hardware implementations targeting any polynomial order. QPA incorporated four key features to minimize the fitting error and the hardware cost, including using the Remez algorithm to compute the minimax fitting polynomial, combining the fitting and quantization operations to get an error-flattened characteristic, assigning specific coefficient bit width to each multiplier to reduce the hardware cost, and fine-tuning the truncated coefficients to further reduce the fitting error. Experimental results showed that our methods consistently achieved the lowest fitting error compared with the state-of-the-art error-flattened piecewise approximation methods. We synthesized the proposed designs with 28-nm TSMC CMOS technology. The results showed that the proposed designs achieved up to 37.0% area reduction and 50.5% power consumption reduction compared to the state-of-the-art error-flattened piecewise linear (PWL) method, and up to 27.0% area reduction, 21.4% delay reduction, and 20.8% power consumption reduction compared to the state-of-the-art error-flattened piecewise quadratic (PWQ) method.

关键词： Fitting Hardware Approximation algorithms Table lookup partitioning algorithms Adders Very large scale integration Approximate computing error-flattened fine-tuning rounding fitting algorithm piecewise polynomial approximation (PPA) prequantization Remez algorithm VLSI architecture

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Construction of Deep ReLU Nets for Spatially Sparse Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7746-7760页

作者： Liu, Xia Wang, Di Lin, Shao-Bo Xian Univ Technol Sch Sci Xian 710048 Peoples R China Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Management Xian 710049 Peoples R China

Training an interpretable deep net to embody its theoretical advantages is difficult but extremely important in the community of machine learning. In this article, noticing the importance of spatial sparseness in signal and image processing, we develop a constructive approach to generate a deep net to capture the spatial sparseness feature. We conduct both theoretical analysis and numerical verifications to show the power of the constructive approach. Theoretically, we prove that the constructive approach can yield a deep net estimate that achieves the optimal generalization error bounds in the framework of learning theory. Numerically, we show that the constructive approach is essentially better than shallow learning in the sense that it provides better prediction accuracy with less training time.

关键词： Deep learning Neural networks Training Spatial resolution partitioning algorithms Signal resolution Optimization Constructive deep net (CDN) deep learning learning theory spatial sparseness

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A Macro-Micro Population-Based Co-Evolutionary Multi-Objective Algorithm for Community Detection in Complex Networks

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2023年第3期18卷 69-86页

作者： Zhang, Lei Yang, Haipeng Yang, Shangshang Zhang, Xingyi Anhui Univ Hefei Peoples R China

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm called MMCoMO for community detection in complex networks to obtain a better trade-off between exploration and exploitation. This algorithm employs two populations, i.e., macro-population and micro-population, for co-evolution to obtain better community structures. In particular, the macro-population prefers exploration and is responsible for quickly determining approximate partitions of the network to obtain good rough community structures as early as possible, whereas the micro-population favors exploitation and is responsible for searching for good fine community structures through the local search process. Thus, these two populations can be used to improve each other through interactions in the co-evolutionary process. In particular, a guiding strategy is designed using the elite (non-dominated) solutions of the macro-population to guide the micro-population, and an influencing strategy is further designed using the elite solutions of the micro-population to positively influence the macro-population. Experiments on synthetic networks and 14 real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms.

关键词： Sociology Complex networks Evolutionary computation Approximation algorithms partitioning algorithms Statistics Detection algorithms

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Parallel Graph Signal Processing: Sampling and Reconstruction

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2023年 9卷 190-206页

作者： Dapena, Daniela Lau, Daniel L. L. Arce, Gonzalo R. R. Univ Delaware Financial Serv Analyt Newark DE 19713 USA Univ Kentucky Elect & Comp Engn Lexington KY 40506 USA Univ Delaware Elect Engn Newark DE 19716 USA

Graph signal processing (GSP) extends classical signal processing methods to analyzing signals supported over irregular grids represented by graphs. Within the scope of GSP, sampling and reconstruction represent fundamental tools that have received considerable attention. For very large graphs, however, many of the current methods struggle with the computational and memory requirements. Vertex domain and randomized sampling strategies somewhat ameliorate the computational requirements, but these algorithms perform poorly at preserving signal fidelity for graphs with hundreds of thousands of vertices. To address these shortcomings, this article introduces a new and scalable approach that can be easily parallelized. This new approach uses existing graph partitioning algorithms in concert with vertex-domain blue-noise sampling and reconstruction, performed independently across partitions. In the reconstruction, some degree of overlapping is added to the partitions to induce trans-partition smoothness in the recovered signal. We also propose two sampling schemes based on the spatial characteristic of the graph that minimizes the recovery error. The first combines graph partitioning with the Void-and-Cluster algorithm, while the second approach uses Error Diffusion. We conclude this article with experiments on synthetic and real data that show the effectiveness of these new approaches on very large graphs.

关键词： partitioning algorithms Approximation algorithms Signal processing algorithms Signal processing Laplace equations Information processing Parallel processing Graph signal reconstruction blue-noise graph signal sampling graph signal processing

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Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot

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IEEE ACCESS 2023年 11卷 29673-29683页

作者： Zhang, Meiyan Cai, Wenyu Pang, Lingfeng Zhejiang Univ Water Resources & Elect Power Coll Elect Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou 310018 Peoples R China

Coverage Path Planning (CPP in short) is a basic problem for mobile robot when facing a variety of applications. Q-Learning based coverage path planning algorithms are beginning to be explored recently. To overcome the problem of traditional Q-Learning of easily falling into local optimum, in this paper, the new-type reward functions originating from Predator-Prey model are introduced into traditional Q-Learning based CPP solution, which introduces a comprehensive reward function that incorporates three rewards including Predation Avoidance Reward Function, Smoothness Reward Function and Boundary Reward Function. In addition, the influence of weighting parameters on the total reward function is discussed. Extensive simulation results and practical experiments verify that the proposed Predator-Prey reward based Q-Learning Coverage Path Planning (PP-Q-Learning based CPP in short) has better performance than traditional BCD and Q-Learning based CPP in terms of repetition ratio and turns number.

关键词： Path planning Mobile robots Predator prey systems Q-learning Planning partitioning algorithms Behavioral sciences Coverage path planning predator-prey model reinforcement learning Q-learning algorithm mobile robot

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Feature Selection Approach Based on Improved Fuzzy C-Means With Principle of Refined Justifiable Granularity

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第7期31卷 2112-2126页

作者： Li, Wentao Zhai, Shichao Xu, Weihua Pedrycz, Witold Qian, Yuhua Ding, Weiping Zhan, Tao Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Shanxi Univ Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

Fuzzy C-means (FCM) is a clustering algorithm based on partition of the universe. However, the partition generated by an equivalence relation is strict in practical application and exhibits relatively poor fault-tolerant mechanism. In this article, a novel binary relation based on improved FCM with the principle of refined justifiable granularity is presented. Different expressions of the proposed binary relation under different values of weight parameter are discussed, and the changes of the properties of the binary relation under different parameter values are provided. By measuring the significance of attributes in the feature space, a feature selection method, called forward heuristic feature selection (FHFS), is designed to construct the low-dimension feature space based on maximizing the original data and information retention through the defined degrees of aggregation and dispersion. It is shown how the results of feature selection and classification performance vary when the values of the weight factor locate in different ranges. To illustrate the superiority and effectiveness of the proposed FHFS algorithm, nine high-dimensional datasets and eight image datasets from University of California-Irvine (UCI) repository are used and compared with other feature selection methods, respectively. The results of experimental evaluation and the significance test show that the proposed learning mechanism is a superior algorithm.

关键词： Feature extraction Clustering algorithms Granular computing partitioning algorithms Loss measurement Dispersion Unsupervised learning Feature selection granular computing (GrC) information granularity justifiable granularity

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Two Channel Filter Banks on Arbitrary Graphs With Positive Semi Definite Variation Operators

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 917-932页

作者： Pavez, Eduardo Girault, Benjamin Ortega, Antonio Chou, Philip A. Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA Univ Rennes ENSAI CNRS CREST UMR F-9194 Rennes France Google Res Seattle WA 98013 USA

We propose novel two-channel filter banks for signals on graphs. Our designs can be applied to arbitrary graphs, given a positive semi definite variation operator, while using arbitrary vertex partitions for downsampling. The proposed generalized filter banks (GFBs) also satisfy several desirable properties including perfect reconstruction and critical sampling, while having efficient implementations. Our results generalize previous approaches that were only valid for the normalized Laplacian of bipartite graphs. Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator. These GFTs are orthogonal in an alternative inner product space which depends on the downsampling and variation operators. Our key theoretical contribution is showing that the spectral folding property of the normalized Laplacian of bipartite graphs, at the core of bipartite filter bank theory, can be generalized for the proposed GFT if the inner product matrix is chosen properly. In addition, we study vertex domain and spectral domain properties of GFBs and illustrate their probabilistic interpretation using Gaussian graphical models. While GFBs can be defined given any choice of a vertex partition for downsampling, we propose an algorithm to optimize these partitions with a criterion that favors balanced partitions with large graph cuts, which are shown to lead to efficient and stable GFB implementations. Our numerical experiments show that partition-optimized GFBs can be implemented efficiently on 3D point clouds with hundreds of thousands of points (nodes), while also improving the color signal representation quality over competing state-of-the-art approaches.

关键词： Laplace equations Bipartite graph Three-dimensional displays Point cloud compression partitioning algorithms Filter banks Approximation algorithms Two-channel filter banks graph Fourier transform graph signal multiresolution representation

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Ordered Escape Routing via Assignment of Routability-Driven Detouring Paths

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2023年第4期13卷 451-464页

作者： Yan, Jin-Tai Tainan Natl Univ Arts Off Res & Dev Tainan 72045 Taiwan

In board-level routing, ordered escape routing (OER) becomes a key problem. In this article, given a set of escape pins inside a pin array, a set of available boundaries, and the capacity constraint between two adjacent pins, an efficient algorithm can be proposed to solve the routing problem in OER under capacity constraints. First, based on the mapping process of the detouring paths of the untangled nets in one-sided net untangling, the routability-driven pins of the untangled nets can be assigned onto feasible positions and the target pins of the escape nets can be assigned on some available boundaries. Furthermore, based on the assignment of the routability-driven detouring paths with the capacity consideration, the global wires of the escape nets in a pin array can be assigned in single-layer global routing and the routing paths of the escape nets in a pin array can be assigned in single-layer detailed routing. Compared with Luo's satisfiability (SAT)-based algorithm, Jiao's flow-based algorithm, and Yan's heuristic algorithm for the six tested examples with capacity as 1, our proposed algorithm can obtain the same 100% routability of the escape nets. Compared with Yan's heuristic algorithm for the other six tested examples with capacity as 2, our proposed algorithm can use reasonable CPU time to improve 1.9% of the total length of the routed escape nets and 0.25% of routability of the routed escape nets on the achievement of the 100% routability on the average.

关键词： Routing Pins Heuristic algorithms Packaging Manufacturing partitioning algorithms Wires Ordered escape routing (OER) pin assignment printed circuit board (PCB) design routability

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BinChill: A Metagenomic Binning Ensemble Method

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IEEE ACCESS 2023年 11卷 49561-49577页

作者： Bak, Oliver S. Jensen, Marcus D. Trudslev, Frederik M. Windfeld, Andreas Lamurias, Andre Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark

The goal of metagenomic binning is to reconstruct genomes from a mixture of DNA sequences into genomic bins, which can be considered a clustering task. Multiple methods have been proposed for this task, such as distance-based metrics, machine learning, and ensemble approaches. We propose BinChill, a metagenomic ensemble method, based on the generic co-occurrence ensembler method, ACE. BinChill incorporates domain information in the form of Single-Copy Genes (SCG) with a co-occurrence strategy. This strategy combines multiple clustering partitions according to how often two items co-occur in the same cluster. BinChill was able to reconstruct more or equally as many high- and medium quality while having an equal or faster runtime than other metagenomics-specific methods on a smaller simulated dataset. On larger datasets, both simulated and real-world, BinChill outperformed other methods in reconstructing high-quality bins, at the cost of an increased processing time when compared to generic ensemble clustering algorithms. This is due to the domain-specific steps that our method implements. Our results show that the strengths of multiple partitions can be combined to generate a partition of higher quality.

关键词： Genomics Ensemble learning partitioning algorithms DNA Pollution measurement Clustering algorithms Bioinformatics clustering algorithms bioinformatics partitioning algorithms

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