Incremental Learning of Concept Drift from Streaming Imbalanced Data

2013 ◽  
Vol 25 (10) ◽  
pp. 2283-2301 ◽  
Author(s):  
Gregory Ditzler ◽  
Robi Polikar
Keyword(s):  
Incremental Learning ◽  
Concept Drift ◽  
Imbalanced Data

Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm

2020 ◽  
Vol 195 ◽  
pp. 105694
Author(s):  
Zeng Li ◽  
Wenchao Huang ◽  
Yan Xiong ◽  
Siqi Ren ◽  
Tuanfei Zhu
Keyword(s):  
Data Streams ◽  
Incremental Learning ◽  
Concept Drift ◽  
Imbalanced Data ◽  
Ensemble Algorithm

A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

2020 ◽  
Vol 31 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Zhe Yang ◽  
Sameer Al-Dahidi ◽  
Piero Baraldi ◽  
Enrico Zio ◽  
Lorenzo Montelatici
Keyword(s):  
Incremental Learning ◽  
Detection Method ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Novel Concept

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 Recursive Ensemble Approach for Incremental Learning of Non-Stationary Imbalanced Data

2014 ◽  
Vol 98 (17) ◽  
pp. 41-45
Author(s):  
Pradnya A.Jain ◽  
Roshani Raut (Ade) ◽  
P. R. Deshmukh
Keyword(s):  
Incremental Learning ◽  
Imbalanced Data ◽  
Ensemble Approach

scholarly journals SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

2018 ◽  
Vol 61 ◽  
pp. 863-905 ◽  
Author(s):  
Alberto Fernandez ◽  
Salvador Garcia ◽  
Francisco Herrera ◽  
Nitesh V. Chawla
Keyword(s):  
Big Data ◽  
Open Source ◽  
Supervised Learning ◽  
Incremental Learning ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Multilabel Classification ◽  
Current State ◽  
Software Packages ◽  
State Of Affairs

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

SENTIMENT CLASSIFICATION WITH CONCEPT DRIFT AND IMBALANCED CLASS DISTRIBUTIONS

2016 ◽  
Vol 78 (12-2) ◽  
Author(s):  
Abbas Jalilvand ◽  
Naomie Salim
Keyword(s):  
Concept Drift ◽  
Imbalanced Data ◽  
Sentiment Classification ◽  
Classification Model ◽  
Training Set ◽  
Classification Framework ◽  
Negative Sentiment ◽  
Document Level ◽  
Independent Identically Distributed ◽  
Over Time

Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, people express their opinions towards an entity based on their characteristics which may change over time. User‘s opinions are changed due to evolution of target entities over time. However, the existing sentiment classification approaches did not considered the evolution of User‘s opinions. They assumed that instances are independent, identically distributed and generated from a stationary distribution, while generated from a stream distribution. They used the static classification model that builds a classifier using a training set without considering the time that reviews are posted. However, time may be very useful as an important feature for classification task. In this paper, a stream sentiment classification framework is proposed to deal with concept drift and imbalanced data distribution using ensemble learning and instance selection methods. The experimental results show the effectiveness of the proposed method in compared with static sentiment classification. 

Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model

2016 ◽  
pp. 561-582
Author(s):  
Pallavi Digambarrao Kulkarni ◽  
Roshani Ade
Keyword(s):  
Learning Strategies ◽  
Real World ◽  
Incremental Learning ◽  
Concept Drift ◽  
Data Distribution ◽  
Class Imbalance ◽  
Learning Approaches ◽  
Stream Data ◽  
Future Data ◽  
Distribution Generation

There are several deep learning approaches that can be applied for analyzing situations in real world problems and inventing their solution in a scientific technique. Supervised data mining methods that predicts instance values, using previously obtained results from already collected data are pretty popular due to their intelligence in machine learning area. Stream data is continuous form of data which can be handled by using incremental learning approach. Stream data learning may face several challenges in real world like concept drift or class imbalance. Concept drift occurs in non-stationary environment where data distribution generation function is dynamic in nature and has no fixed formula to predict the future data distribution nature. Neural network techniques are intelligent enough to improve performance of algorithmic systems that work in such problem domains. This chapter briefly describes how MLP technique is integrated in system so that the system becomes a complete framework for handling unbalanced data with concept drift in the incremental learning strategies.

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