Search Results for author: Zengyou He

Found 8 papers, 4 papers with code

Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification

no code implementations16 Oct 2023 Junjie Dong, Mudi Jiang, Lianyu Hu, Zengyou He

Existing pattern-based methods measure the discriminative power of each feature individually during the mining process, leading to the result of missing some combinations of features with discriminative power.

Classification

Interpretable Sequence Clustering

1 code implementation3 Sep 2023 Junjie Dong, Xinyi Yang, Mudi Jiang, Lianyu Hu, Zengyou He

Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges.

Clustering

A testing-based approach to assess the clusterability of categorical data

1 code implementation14 Jul 2023 Lianyu Hu, Junjie Dong, Mudi Jiang, Yan Liu, Zengyou He

The objective of clusterability evaluation is to check whether a clustering structure exists within the data set.

Attribute Clustering +1

Personalized Interpretable Classification

no code implementations6 Feb 2023 Zengyou He, Yifan Tang, Lianyu Hu, Mudi Jiang, Yan Liu

In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample.

Classification

Significance-Based Categorical Data Clustering

1 code implementation8 Nov 2022 Lianyu Hu, Mudi Jiang, Yan Liu, Zengyou He

As a by-product, we can further calculate an empirical $p$-value to assess the statistical significance of a set of clusters and develop an improved gap statistic for estimating the cluster number.

Clustering

Reference-Based Sequence Classification

no code implementations17 May 2019 Zengyou He, Guangyao Xu, Chaohua Sheng, Bo Xu, Quan Zou

By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions.

Classification General Classification

Instance-Based Classification through Hypothesis Testing

no code implementations3 Jan 2019 Zengyou He, Chaohua Sheng, Yan Liu, Quan Zou

After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value.

Binary Classification Classification +2

A Fast Greedy Algorithm for Outlier Mining

1 code implementation Advances in Knowledge Discovery and Data Mining 2006 Zengyou He, Shengchun Deng, Xiaofei Xu, Joshua Zhexue Huang

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data.

Outlier Detection

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