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The efficiency (prediction accuracy) of a classification model is affected by the quality of training data. High dimensionality and class imbalance are two main problems that may cause low quality of training datasets, making data preprocessing a very important step for a classification problem. Feature (software metric) selection and data sampling are frequently used to overcome these problems. Feature...
One of the main characteristics of bioinformatics datasets is noise. Noise refers to incorrect or missing values in a dataset and has a detrimental effect on classification. In this study we evaluate the robustness of six classification algorithms and ten filter-based feature selection techniques, specifically to study how the different techniques are impacted by particularly challenging datasets...
Online shopping websites provide platforms for consumers to review products and share opinions. Online reviews provided by the previous consumers are major information source for both consumers and marketers. However, a large number of reviews for a product can make it impossible for readers to read through all the reviews in order to collect information. So it is important to classify and rank the...
Bioinformatics datasets pose two major challenges to researchers and data-mining practitioners: class imbalance and high dimensionality. Class imbalance occurs when instances of one class vastly outnumber instances of the other class(es), and high dimensionality occurs when a dataset has many independent features (genes). Data sampling is often used to tackle the problem of class imbalance, and the...
Firewalls form an essential element of modern network security, detecting and discarding malicious packets before they can cause harm to the network being protected. However, these firewalls must process a large number of packets very quickly, and so can't always make decisions based on all of the packets' properties (features). Thus, it is important to understand which features are most relevant...
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