scholarly journals A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ge Song ◽  
Yunming Ye
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Concept Drift ◽  
Real Life ◽  
Class Imbalance ◽  
High Dimensional ◽  
Adaptive Selection ◽  
Stream Classification ◽  
Rare Class

Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sai Kiranmayee Samudrala ◽  
Jaroslaw Zola ◽  
Srinivas Aluru ◽  
Baskar Ganapathysubramanian
Keyword(s):  
Dimensionality Reduction ◽  
Organic Solar Cells ◽  
Large Scale ◽  
Parallel Implementation ◽  
High Dimensional Data ◽  
Real Life ◽  
Processing Parameters ◽  
High Dimensional ◽  
Morphology Evolution ◽  
Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

scholarly journals Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

2021 ◽  
Author(s):  
S. Priya ◽  
R. Annie Uthra
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Concept Drift ◽  
Class Imbalance ◽  
Streaming Data ◽  
Superior Performance ◽  
Data Streaming ◽  
Minority Class ◽  
Concept Drift Detection

AbstractIn present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

scholarly journals LICIC: Less Important Components for Imbalanced Multiclass Classification

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 317 ◽  
Author(s):  
Vincenzo Dentamaro ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Gene Expression ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Multiclass Classification ◽  
Cancer Diagnostics ◽  
Mass Spectrometry Data ◽  
High Dimensional ◽  
Or Gene ◽  
High Dimensional Datasets

Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.

Weighted Similarity Classifier Using Differential Evolution and Genetic Algorithm in Weight Optimization

2004 ◽  
Vol 8 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Pasi Luukka ◽  
◽  
Jouni Sampo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Similarity Measures ◽  
High Dimensional ◽  
Weight Optimization ◽  
Large Scale Data ◽  
Data Weighting

We have compared the differential evolution and genetic algorithms in a study of weight optimization for different similarity measures in a task of classification. In a study of high dimensional data weighting similarity measures become of great importance and efforts to study suitable optimizers is needed. In this article we have studied proper weighting of similarity measures in the classification of high dimensional and large scale data. We will show that in most cases the differential evolution algorithm should be used in finding the weights instead of the genetic algorithm.

scholarly journals Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yange Sun ◽  
Meng Li ◽  
Lei Li ◽  
Han Shao ◽  
Yi Sun
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Learning Strategy ◽  
Concept Drift ◽  
Class Imbalance ◽  
Data Preprocessing ◽  
Cost Information ◽  
Detection Mechanism ◽  
Stream Classification ◽  
Data Stream Classification

Class imbalance and concept drift are two primary principles that exist concurrently in data stream classification. Although the two issues have drawn enough attention separately, the joint treatment largely remains unexplored. Moreover, the class imbalance issue is further complicated if data streams with concept drift. A novel Cost-Sensitive based Data Stream (CSDS) classification is introduced to overcome the two issues simultaneously. The CSDS considers cost information during the procedures of data preprocessing and classification. During the data preprocessing, a cost-sensitive learning strategy is introduced into the ReliefF algorithm for alleviating the class imbalance at the data level. In the classification process, a cost-sensitive weighting schema is devised to enhance the overall performance of the ensemble. Besides, a change detection mechanism is embedded in our algorithm, which guarantees that an ensemble can capture and react to drift promptly. Experimental results validate that our method can obtain better classification results under different imbalanced concept drifting data stream scenarios.

scholarly journals A Novel Approach to Maximize G-mean in Nonstationary Data with Recurrent Imbalance Shifts

2020 ◽  
Vol 18 (1) ◽  
pp. 103-113
Keyword(s):  
State Of The Art ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Streaming Data ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Boosting Algorithms ◽  
Nonstationary Data ◽  
Test Outcomes

One of the noteworthy difficulties in the classification of nonstationary data is handling data with class imbalance. Imbalanced data possess the characteristics of having a lot of samples of one class than the other. It, thusly, results in the biased accuracy of a classifier in favour of a majority class. Streaming data may have inherent imbalance resulting from the nature of dataspace or extrinsic imbalance due to its nonstationary environment. In streaming data, timely varying class priors may lead to a shift in imbalance ratio. The researchers have contemplated ensemble learning, online learning, issue of class imbalance and cost-sensitive algorithms autonomously. They have scarcely ever tended to every one of these issues mutually to deal with imbalance shift in nonstationary data. This correspondence shows a novel methodology joining these perspectives to augment G-mean in no stationary data with Recurrent Imbalance Shifts (RIS). This research modifies the state-of-the-art boosting algorithms,1) AdaC2 to get G-mean based Online AdaC2 for Recurrent Imbalance Shifts (GOA-RIS) and AGOA-RIS (Ageing and G-mean based Online AdaC2 for Recurrent Imbalance Shifts), and 2) CSB2 to get G-mean based Online CSB2 for Recurrent Imbalance Shifts (GOC-RIS) and Ageing and G-mean based Online CSB2 for Recurrent Imbalance Shifts (AGOC-RIS). The study has empirically and statistically analysed the performances of the proposed algorithms and Online AdaC2 (OA) and Online CSB2 (OC) algorithms using benchmark datasets. The test outcomes demonstrate that the proposed algorithms globally beat the performances of OA and OC

scholarly journals Classification of Sentiment Based on Movie Feedback Given By Audiences

2019 ◽  
Vol 8 (4) ◽  
pp. 2594-2602
Keyword(s):  
Confusion Matrix ◽  
Real Life ◽  
Class Imbalance ◽  
Extraction Methods ◽  
Class Imbalance Problem ◽  
Inverse Document Frequency ◽  
Imbalance Problem ◽  
Life Problems ◽  
Document Frequency

The need for generating automated sentiment on audience feedbacks has been the need of the hour. Manually going through the entire movie feedback becomes tedious therefore an attempt to predict the polarity of a movie based on the reviews using machine learning models is done. Usage of the IMDB movie reviews dataset has been done for training and testing. In this study we also try to depict the real-life problems of class imbalance and train-test splits, hence obtaining solutions for the same. The problem of class imbalance in today’s world has affected a large amount of predictive applications such as cancer detection , fraudulent transactions in banks etc, hence this study is an attempt to perform a solution to solve the class imbalance problem. Use of the undersampling method has been done in this study to improve the accuracy of an imbalanced class. Feature extraction methods such as Bag of Words and Term Frequency Inverse document Frequency have been used to generate features from the reviews. The Logistic regression and SVM classifiers have been used in the study to measure the accuracy. Along with the accuracy the Confusion Matrix has also been calculated to showcase the class imbalance taking its effect on the accuracy.

scholarly journals High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

2011 ◽  
Vol 5 ◽  
Author(s):  
Ramon Casanova ◽  
Christopher T. Whitlow ◽  
Benjamin Wagner ◽  
Jeff Williamson ◽  
Sally A. Shumaker ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Mri ◽  
High Dimensional ◽  
Dimensional Classification
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