Latest developments in computing and technology, along with the availability of large amounts of raw data, have led to the development of many computational techniques and algorithms. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. Logis...

Associative Classification (AC) is a well known tool in knowledge discovery and it has been proved to extract competitive classifiers. However, imbalanced data has posed a challenge for most classifier learn ing algorithms including AC methods. Because in the AC process, Interestingness Measure (IM) p lays an important role to generate interesting rules and build good classifiers, it is very im...

• Proposal of potential resemblance loss for measuring relative class distribution shape. unified over and undersampling framework based on resemblance. data difficulty index evaluation dataset complexity. Experimental the proposed approach. Examination factors influencing performance Data imbalance remains one negatively affecting contemporary machine learning algorithms. One most common appro...

Non-invasive visual detection of skin cancers from benign tumors remains a challenge in clinical practice. Studies have claimed the non-inferiority artificial intelligence classifying common such as nevus and melanoma. Better algorithms are yet to be developed assist accurate diagnoses. The aim this current study is investigate whether small or limited sample size for AI training could achieve ...

In supervised classification, we often encounter many real world problems in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. One of the most used techniques to deal with this problem consists of preprocessing the data previously to the learning process. This paper proposes a m...

BACKGROUND Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally no...

The scenario of classification with imbalanced data-sets has supposed a serious challenge for researchers along the last years. The main handicap is related to the large number of real applications in which one of the classes of the problem has a few number of examples in comparison with the other class, making it harder to be correctly learnt and, what is most important, this minority class is...

Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents. Many of classification algorithms usually assume that the training examples are evenly distributed among different classes. However, unbalanced data sets often appear in many practical applications. In order to deal with uneven text sets, we propose the neighbor-wei...

Support Vector Machines (SVMs) is a popular machine learning technique, which has proven to be very effective in solving many classical problems with balanced data sets in various application areas. However, this technique is also said to perform poorly when it is applied to the problem of learning from heavily imbalanced data sets where the majority classes significantly outnumber the minority...