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This study is carried out on application of different machine learning techniques for prediction of features using bioinformatics data such as that of cancer. The prediction process is undertaken based on feature extraction and then on feature selection process. After selecting the relevant features, machine learning techniques like Support vector machine (SVM), Extreme learning machine (ELM) are...
The disease Leukemia are continuously increasing among the people. The cause of leukemia is unknown but several factors, however are associated with the development of leukemia that are exposure to ionising radiation, exposure to benzene in rubber industry workers, cytotoxic drug particularly alkylating agent exposure, genetic disorder like down syndrome and immunological deficiency states. There...
Support Vector Machines are the state-of-the-art tools in data mining. However, their strength are also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. Therefore, opening the black-boxor making SVMs explainable became more important and necessary in areas such as medical diagnosis and credit evaluation. Rule extraction from SVMs,...
A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable...
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance...
We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of ``normal'' training data points in a chosen...
The increasing numbers of Web pages on the cyber world result to the less effectiveness of document retrieval that matches the need of users. The classification of Web pages is one of the solutions to solve this problem. This paper proposes VAMSVM_WPC model which is a novel voting algorithm for classifying the Web pages, which uses a multi-class SVM method. First, feature is generated from text and...
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