25 Result(s)
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Chapter and Conference Paper
Involving Society to Protect Society from Fake News and Disinformation: Crowdsourced Datasets and Text Reliability Assessment
Detecting fake information is a complex and multi-faceted challenge concerning data gathering, processing, analysis and detection, as well as countering fake content distribution. On the one hand, fake content...
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Chapter and Conference Paper
Hollow n-grams Vectorizer for Natural Language Processing Problems
Words don’t come easy, which fosters the use of generative artificial intelligence models in ongoing popularity of widely available applications such as ChatGPT. The result is an even greater flood of online c...
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Chapter and Conference Paper
Incremental Extreme Learning Machine for Binary Data Stream Classification
Classifier ensembles have shown the ability to classify drifted data streams. The following paper proposes an ensemble consisting of a single hidden layer feedforward neural network and an Extreme Learning Mac...
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Chapter and Conference Paper
SWAROG Project Approach to Fake News Detection Problem
We often come across the seemingly obvious remark that the modern world is full of data. From the perspective of a regular Internet user, we perceive this as an abundance of content that we unintentionally con...
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Chapter and Conference Paper
Analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy
Bulk construction of pattern classifiers, whether for optimizing input data configurations or method hyperparameters, is a computationally highly complex task. The main problem is the prediction quality evalua...
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Chapter and Conference Paper
Analysis of Variance Application in the Construction of Classifier Ensemble Based on Optimal Feature Subset for the Task of Supporting Glaucoma Diagnosis
The following work aims to propose a new method of constructing an ensemble of classifiers diversified by the appropriate selection of the problem subspace. The experiments were performed on a numerical datase...
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Chapter and Conference Paper
Sentiment Analysis for Fake News Detection by Means of Neural Networks
The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be...
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Chapter and Conference Paper
Combination of Active and Random Labeling Strategy in the Non-stationary Data Stream Classification
A significant problem when building classifiers based on data stream is information about the correct label. Most algorithms assume access to this information without any restrictions. Unfortunately, this is n...
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Chapter and Conference Paper
Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification
Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level...
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Chapter and Conference Paper
Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks
The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that ...
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Chapter and Conference Paper
Machine Learning Methods for Fake News Classification
The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be...
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Chapter and Conference Paper
Classifier Selection for Highly Imbalanced Data Streams with Minority Driven Ensemble
The nature of analysed data may cause the difficulty of the many practical data mining tasks. This work is focusing on two of the important research topics associated with data analysis, i.e., data stream clas...
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Chapter and Conference Paper
SMOTE Algorithm Variations in Balancing Data Streams
From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increas...
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Chapter and Conference Paper
Imbalance Reduction Techniques Applied to ECG Classification Problem
In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches ...
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Chapter and Conference Paper
A Genetic-Based Ensemble Learning Applied to Imbalanced Data Classification
Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data cl...
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Chapter and Conference Paper
Imbalanced Data Classification Based on Feature Selection Techniques
The difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canoni...
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Chapter and Conference Paper
Combined Classifier Based on Quantized Subspace Class Distribution
Following paper presents Exposer Ensemble (ee), being a combined classifier based on the original model of quantized subspace class distribution. It presents a method of establishing and processing the Planar Exp...
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Chapter and Conference Paper
A First Attempt to Construct Effective Concept Drift Detector Ensembles
The big data is usually described by so-called 5Vs (Volume, Velocity, Variety, Veracity, Value). The business success in the big data era strongly depends on the smart analytical software which can help to mak...
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Chapter and Conference Paper
Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis
Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human...
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Chapter and Conference Paper
Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study
For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support softwar...
