Incremental learning framework for real‐world fraud detection environment

2021 ◽  
Vol 37 (1) ◽  
pp. 635-656
Author(s):  
Farzana Anowar ◽  
Samira Sadaoui
Keyword(s):  
Real World ◽  
Incremental Learning ◽  
Fraud Detection ◽  
Learning Framework

Incremental learning strategies for credit cards fraud detection: Extended abstract

2020 ◽  
Author(s):  
Bertrand Lebichot ◽  
Gian Marco Paldino ◽  
Gianluca Bontempi ◽  
Wissam Siblini ◽  
Liyun He-Guelton ◽  
...  
Keyword(s):  
Learning Strategies ◽  
Incremental Learning ◽  
Credit Cards ◽  
Fraud Detection

scholarly journals Incremental Learning Framework for Mining Big Data Stream

2022 ◽  
Vol 71 (2) ◽  
pp. 2901-2921
Author(s):  
Alaa Eisa ◽  
Nora EL-Rashidy ◽  
Mohammad Dahman Alshehri ◽  
Hazem M. El-bakry ◽  
Samir Abdelrazek
Keyword(s):  
Big Data ◽  
Incremental Learning ◽  
Data Stream ◽  
Learning Framework

scholarly journals Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

2020 ◽  
Author(s):  
Cong Fei ◽  
Bin Wang ◽  
Yuzheng Zhuang ◽  
Zongzhang Zhang ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
Data Augmentation ◽  
State Of The Art ◽  
Robot Learning ◽  
Imitation Learning ◽  
Proper Understanding ◽  
Learning Framework ◽  
Strategy Game ◽  
Real Time Strategy Game

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

scholarly journals Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6550 ◽  
Author(s):  
Chen Cheng ◽  
Ji Chang ◽  
Wenjun Lv ◽  
Yuping Wu ◽  
Kun Li ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Classification Accuracy ◽  
Concept Drift ◽  
Dynamic Environment ◽  
Temporal Correlation ◽  
Classification Methods ◽  
Terrain Classification ◽  
Localization Accuracy ◽  
Learning Framework ◽  
Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

scholarly journals Lifelong Spectral Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5867-5874
Author(s):  
Gan Sun ◽  
Yang Cong ◽  
Qianqian Wang ◽  
Jun Li ◽  
Yun Fu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Spectral Clustering ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Orthogonal Basis ◽  
Learning Framework ◽  
The Past ◽  
Benchmark Datasets ◽  
Over Time

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.

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