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Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. It is the second most common cause of cancer death in women. It is common disease in India and accounted that 25% to 31% is the breast cancer in women in Indian cities. Breast cancer is a disease in which malignant (cancer) cells form in the tissues of the breast. The damaged cells can invade...
Attribute reduction of an information system is a key problem in rough set theory and its applications. This paper proposes a new feature selection mechanism based on backward elimination algorithm to solve the attribute reduction problem in roughest theory. It is the most promising technique in the Rough set theory, a new mathematical approach to reduct car and cancer dataset using backward elimination...
Outlier detection is an important field in data mining and knowledge discovery, which aims to identify abnormal observations in a large dataset. Common application areas of outlier detection are intrusion detection in computer networks, credit cards fraud detection, detecting abnormal changes in stock prices, and identifying abnormal health conditions. We propose the use of a novel swarm intelligence...
It is interesting to discover exceptions, as they dispute the existing knowledge and have elements of unexpectedness and surprise. As exceptions focus on a very small portion of data, discovering exceptions still remains a great challenge. A censored production rule (CPR) is a special kind of knowledge structure that augments exceptions to their corresponding commonsense rules of high generality and...
The requires an integrative biological systems analysis as the quantitative description at the hierarchical level of molecular, cellular and phenotypic functions including their interaction with the environment is very complex. The growing awareness of the complex interplay between the genome and physiological functions of the cell needs a new holistic and full integrative view. In this paper we present...
Due to the learning problem on skewed distribution datasets, which tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class by traditional machine learning algorithms, fuzzy information granulation based knowledge discovery and decision support model called FIG mode is proposed in this paper to improve classification performance and make effective...
Cells are able to use the bottom-up information (genome versus metabolic pathway) but also top-down environment/gene mutation. Our approach considers the information emergency from the sub-systems to the whole system. In these conditions an unified analytical model is very difficult to build up. Therefore an abstract model can be very helpful for prognosis diagnostic in medical science. We enhance...
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