The paper considers model selection in regression under the additional structural constraints on admissible models where the number of potential predictors miht be even larger than the available sample size. We develop a Bayesian formalism which is used as a natural tool for generating a wide class of model selection criteria based on penalized least squares estimation with various complexity p...

in this paper, we propose a new gene selection algorithm based on shuffled frog leaping algorithm that is called sfla-fs. the proposed algorithm is used for improving cancer classification accuracy. most of the biological datasets such as cancer datasets have a large number of genes and few samples. however, most of these genes are not usable in some tasks for example in cancer classification. ...

since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. there are 2 ways to stop this disease from developing. one way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. the presented approach...

a full investigation into the features extracted from voice signals of people with and without parkinson’s disease was performed. a total of 31 people with and without the disease participated in the data collection phase. their voice signals were recorded and processed. the relevant features were then extracted. a variety of feature selection methods have been utilized resulting in a good perf...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

Gene expression profiles have great potential as a medical diagnostic tool since they represent the state of a cell at the molecular level. Available training data sets for classification of cancer types generally have a fairly small sample size compared to the number of genes involved. This fact poses an insurmountable problem to some classification methodologies due to training data limitatio...

with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

breast cancer is the most common type of cancer among women. the important key to treat the breast cancer is early detection of it because according to many pathological studies more than 80% of all abnormalities are still benign at primary stages; so in recent years, many studies and extensive research done to early detection of breast cancer with higher precision and accuracy.infra-red breast...

bivariate features, obtained from multichannel electroencephalogram (eeg) recordings, quantify the relation between different brain regions. studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios ...

In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection. For example, the k-nearest-neighbors classification procedure yields a much higher accuracy when applied on variables with high discriminatory power. Typical (univariate) variable selection methods for binary classification are, e.g....