-
DC-NAS: Divide-and-Conquer Neural Architecture Search for Multi-Modal Classification
Xinyan Liang, Pinhan Fu, Qian Guo, Keyin Zheng, Yuhua Qian
Neural architecture search-based multi-modal classification (NAS-MMC) methods can individually obtain the optimal classifier for different multi-modal data sets in an automatic manner.
AAAI'24
-
Generalization Performance of Pure Accuracy and Its Application in Selective Ensemble Learning
Jieting Wang, Yuhua Qian, Feijiang Li, Jiye Liang, Qingfu Zhang
The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023,45(2),1798-1816
-
Incremental Learning for Simultaneous Augmentation of Feature and Class
Chenping Hou,Shilin Gu, Chao Xu, Yuhua Qian
With the emergence of new data collection ways in many dynamic environment applications, the samples are gathered gradually in the accumulated feature spaces.
IEEE Transactions on Pattern Analysis and Machine Intelligence,DOI 10.1109/TPAMI.2023.3307670
-
ESSR: Evolving Sparse Sharing Representation for Multi-task Learning
Yayu Zhang,Yuhua Qian,Guoshuai Ma, Xinyan Liang, Guoqing Liu, Qingfu Zhang,Ke Tang
Abstract—Multi-task learning uses knowledge transfer among tasks to improve the generalization performance of all tasks. For deep multi-task learning, knowledge transfer is often implemented via sharing all hidden features of tasks.
IEEE Transactions on Evolutionary Computation,2023,DOI 10.1109/TEVC.2023.3272663
-
AF: An Association-based Fusion Method for Multi-Modal Classification
Xinyan Liang, Yuhua Qian, Qian Guo, Honghong Cheng, Jiye Liang
Multi-modal classifification (MMC) aims to integrate the complementary information from different modalities to improve classifification performance.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12):9236-9254.
-
Neighborhood Information-based Method for Multivariate Association Mining
Honghong Cheng, Yuhua Qian, Yingjie Guo, Keyin Zheng, Qingfu Zhang
Most current data is multivariable, exploring and identifying valuable information in these datasets has far-reaching impacts. In particular, discovering meaningful hidden association patterns in multivariate plays an important role.
IEEE Transactions on Knowledge and Data Engineering,2022,In Press
-
Local Feature Selection for Large-scale Data Sets with Limited Labels
Tian Yang, Yanfang Deng, Bin Yu, Yuhua Qian, Jianhua Dai
Processing large-scale data sets with limited labels has always been a difficult task in data mining. Facing this difficulty, two local feature selection algorithms, LARD and LRSD, have been proposed based on dependency degree,
IEEE Transactions on Knowledge and Data Engineering,2022,In Press
-
A unified sample selection framework for output noise filtering: an error-bound perspective
Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang
The existence of output noise will bring difficulties to supervised learning. Noise filtering,aiming to detect and remove polluted samples, is one of the main ways to deal with the noise on outputs.
Journal of Machine Learning Research, 2021, 22, 1-66.
-
Enhanced group sparse regularized nonconvex regression for face recognition
Chao Zhang, Huaxiong Li, Chunlin Chen, Yuhua Qian, Xianzhong Zhou
Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex l1-norm and nuclear norm are usually utilized to approximate the l0-norm and rank function.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, In Press
-
Evolutionary Deep Fusion Method and Its Application in Chemical Structure Recognition
Xinyan Liang, Qian Guo, Yuhua Qian, Weiping Ding, Qingfu Zhang
Abstract—Feature extraction is a critical issue in many machine learning systems. A number of basic fusion operators have been proposed and studied. This paper proposes an evolutionary algorithm, called evolutionary deep fusion ...
IEEE Transactions on Evolutionary Computation, 2021, Accepted
-
Learning with mitigating random consistency from the accuracy measure
Jieting Wang, Yuhua Qian, Feijiang Li
Human beings may make random guesses in decision-making. Occasionally, their guesses may generate consistency with the real situation.This kind of consistency is termed random consistency. In the area of machine leaning, the randomness...
Machine Learning, 2020, 109: 2247–2281.
-
成红红, 钱宇华, 胡治国, 梁吉业
识别海量变量间潜在的复杂关联关系,判断不同形式关联关系的强弱,是大数据关联关系挖掘的重要任务之一.然而,数据分布的不确定性、关联关系的多样性,使得基于分布假设的关联关系度量和基于数据驱动的非参数度量方法的适用性、准确性难以保证.
中国科学: 信息科学, 2020, 50(6): 824-844.
-
李飞江, 钱宇华, 王婕婷, 梁吉业, 王文剑
数据类型和分布的复杂化导致样本间关系的不确定性增强,给有效挖掘数据的潜在类簇结构带来挑战.为降低样本关系不确定性对数据聚类带来的影响,本文将聚类集成中样本稳定性概念扩展至聚类分析中.本文从理论上分析样本稳定的合理性,并提出基于信息熵的样本稳定性度量方法.
中国科学: 信息科学, 2020, 50(8): 1239-1254.
-
Adaptive Local Low-rank Matrix Approximation for Recommendation
Huafeng Liu, Liping Jing, Yuhua Qian, Jian Yu
Low-rank matrix approximation (LRMA) has attracted more and more attention in the community of recommendation. Even though LRMA-based recommendation methods (including Global LRMA and Local LRMA) obtain promising results, ...
ACM Transactions on Information Systems, 2019, 37(4), 45.
-
Intuitionistic fuzzy rough set-based granular structures and attribute subset selection
Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li
Attribute subset selection is an important issue in dataminingandinformationprocessing.However,mostautomatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors.
IEEE Transactions on Fuzzy Systems, 2019, 27(3), 527-539.
-
Clustering ensemble based on sample's stability
Feijiang Li, Yuhua Qian, Jieting Wang, Chuangyin Dang, Liping Jing
The objective of clustering ensemble is to fifind the underlying structure of data based on a set of clustering results. It has been observed that the samples can change between clusters in different clustering results. This change shows that samples may have different contributions to the detection of the underlying structure.
Artificial Intelligence, 2019, 273, 37-55.
-
Identifying advisor-advisee relationships from co-author networks via a novel deep model
Zhongying Zhao, Wenqiang Liu, Yuhua Qian, Liqiang Nie, Yilong Yin, Yong Zhang
Advisor-advisee is one of the most important relationships in research publication networks. Identifying it can benefit many interesting applications, such as double-blind peer review, academic circle mining, and scientific community analysis.
Information Sciences, 2018, 466, 258-269.
-
A quantitative approach to reasoning about incomplete knowledge
Yanhong She, Xiaoli He, Yuhua Qian, Weihua Xu, Jinhai Li
In this paper, we aim to present a quantitative approach to reasoning about incomplete information. The study is conducted in MEL, a minimal epistemic logic relating modal languages to uncertainty theories.
Information Sciences, 2018, 451-452, 100-111.
-
Local rough set:a solution to rough data analysis in big data
Yuhua Qian, Xinyan Liang, Qi Wang, et al
As a supervised learning method, classical rough set theory often requires a large amount of labeled data, in which concept approximation and attribute reduction are two key issues.
International Journal of Approximate Reasoning,2018, 97，38-63.
-
Multichannel Consistent Sampling and Reconstruction associated with Linear Canonical Transform
Liyun Xu, Ran Tao, Feng Zhang
Multichannel sampling is fundamental in the theory of multichannel parallel analog-to-digital converters (ADC) and multiplexing wireless communication. This letter investigates the multichannel consistent
IEEE signal processing letters,2017, 24(5): 658-662.
-
Fractional spectral analysis of randomly sampled signals and applications
Liyun Xu, Feng Zhang, Ran Tao
Nonuniform sampling can be utilized to achieve certain desirable results. Periodic nonuniform sampling can decrease the required sampling rate for signals. Random sampling can be used as a digital alias-free signal
IEEE transactions on Instrumentation & Measurement,2017 ,99, 1-13 .
-
Fusing complete monotonic decision trees
Hang Xu, Wenjian Wang, Yuhua Qian
Monotonic classification is a kind of classification task in which a monotonicity constraint exist between features and class, i.e., if sample xi has a higher value in each feature than sample xj
IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10), 2223 - 2235.
-
A fitting model for feature selection with fuzzy rough sets
Changzhong Wang, Yali Qi, Mingwen Shao, Qinghua Hu, Degang Chen, Yuhua Qian, Yaojin Lin.
Fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a maximal dependency function.
IEEE Transactions on Fuzzy Systems, 2016 (In Press).
-
Space structure and clustering of categorical data.
Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, Chuangyin Dang
Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects
IEEE Transactions on Neural Networks and Learning Systems ,2016, 27(10): 2047-2059.
-
Zhiqiang Wang, Jiye Liang, Ru Li, Yuhua Qian
Cold-start link prediction is a term for information starved link prediction where little or no topological information is present to guide the determination of whether links to a node will form.
IEEE Transactions on Knowledge and Data Engineering,2016, 28(11), 2857-2870.