Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection

2019 ◽  
Vol 109 ◽  
pp. 153-164 ◽  
Author(s):  
Carlos Mera ◽  
Mauricio Orozco-Alzate ◽  
John Branch
Keyword(s):  
Incremental Learning ◽  
Visual Inspection ◽  
Concept Drift ◽  
Multiple Instance Learning

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.

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.

Incremental Learning on Non-stationary Data Stream using Ensemble Approach

2016 ◽  
Vol 6 (4) ◽  
pp. 1811 ◽  
Author(s):  
Meenakshi Anurag Thalor ◽  
Shrishailapa Patil
Keyword(s):  
Machine Learning ◽  
Incremental Learning ◽  
Data Stream ◽  
Recommendation System ◽  
Concept Drift ◽  
Joint Probability ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Joint Probability Distribution ◽  
Changes Over Time

<span lang="EN-US">Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer’s behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data  is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mean</span>

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