paper-with-me

홈 › Papers

OVT-MLCS: An Online Visual Tool for MLCS Mining from Long or Big Sequences

2026-01-09 · Zhi Wang, Yanni Li, Tihua Duan, Bing Liu, Liyong Zhang, Hui Li arxiv

Mining multiple longest common subsequences (\textit{MLCS}) from a set of sequences of three or more over a finite alphabet $Σ$ (a classical NP-hard problem) is an important task in a wide variety of application fields. Unfortunately, there is still no exact \textit{MLCS} algorithm/tool that can handle long (length $\ge$ 1,000) or big (length $\ge$ 10,000) sequences, which seriously hinders the development and utilization of massive long or big sequences from various application fields today. To address the challenge, we first propose a novel key point-based \textit{MLCS} algorithm for mining big sequences, called \textit{KP-MLCS}, and then present a new method, which can compactly represent all mined \textit{MLCSs} and quickly reveal common patterns among them. Furthermore, by introducing some new techniques, e.g., real-time graphic visualization and serialization, we have developed a new online visual \textit{MLCS} mining tool, called OVT-MLCS. OVT-MLCS demonstrates that it not only enables effective online mining, storing, and downloading of \textit{MLCSs} in the form of graphs and text from long or big sequences with a scale of 3 to 5000 but also provides user-friendly interactive functions to facilitate inspection and analysis of the mined \textit{MLCS}s. We believe that the functions provided by OVT-MLCS will promote stronger and wider applications of \textit{MLCS}.

📄 PDF Abstract BibTeX arXiv:2604.13037

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI development

2026-04-09 · Ioannis Nasios arxiv

Machine learning competitions (MLCs) play a pivotal role in advancing artificial intelligence (AI) by fostering innovation, skill development, and practical problem-solving. This study provides a comprehensive analysis o…

Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge

2017-08-25 · Xinyang Zhang, Yujie Ji, Ting Wang

Many of today's machine learning (ML) systems are not built from scratch, but are compositions of an array of {\em modular learning components} (MLCs). The increasing use of MLCs significantly simplifies the ML system de…

Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search

2023-01-31 · Sepehr Sharifi, Donghwan Shin, Lionel C. Briand, Nathan Aschbacher

In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms o…

Digging Deeper: Learning Multi-Level Concept Hierarchies

2026-03-10 · Oscar Hill, Mateo Espinosa Zarlenga, Mateja Jamnik arxiv

Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent…

Hierarchical Partitioning of the Output Space in Multi-label Data

2016-12-19 · Yannis Papanikolaou, Ioannis Katakis, Grigorios Tsoumakas

Hierarchy Of Multi-label classifiers (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and s…

ClusteringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning