نتایج جستجو برای: hierarchical feature selection fs

تعداد نتایج: 619574  

Journal: :Concurrency and Computation: Practice and Experience 2023

Abstract In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve above problems, we propose an online selection algorithm based on adaptive ReliefF. Firstly, ReliefF adaptively improved via using density information instances around target sample, making it unnecessary to prespecify parameters. Secondly, relation...

2006
Roland Nilsson José M. Peña Johan Björkegren Jesper Tegnér

We perform a systematic evaluation of feature selection (FS) methods for support vector machines (SVMs) using simulated high-dimensional data (up to 5000 dimensions). Several findings previously reported at low dimensions do not apply in high dimensions. For example, none of the FS methods investigated improved SVM accuracy, indicating that the SVM built-in regularization is sufficient. These r...

Journal: :iranian journal of diabetes and obesity 0
razieh sheikhpour school of electrical and computer engineering, yazd university, yazd, iran. mehdi agha sarram school of electrical and computer engineering, yazd university, yazd, iran.

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...

Journal: :Pattern Recognition Letters 2016
Vural Aksakalli Milad Malekipirbazari

Feature selection (FS) has become an indispensable task in dealing with today’s highly complex pattern recognition problems with massive number of features. In this study, we propose a new wrapper approach for FS based on binary simultaneous perturbation stochastic approximation (BSPSA). This pseudo-gradient descent stochastic algorithm starts with an initial feature vector and moves toward the...

2014
Ziming Zhang Heng Huang Dinggang Shen

In this paper, we explore the effects of integrating multi-dimensional imaging genomics data for Alzheimer's disease (AD) prediction using machine learning approaches. Precisely, we compare our three recent proposed feature selection methods [i.e., multiple kernel learning (MKL), high-order graph matching based feature selection (HGM-FS), sparse multimodal learning (SMML)] using four widely-use...

2012
R. K. Bania B. Borah

Extracting useful information from a huge data collection is an important and challenging issue. Feature selection (FS) refers to the problem of selecting minimal relevant features which produce the most predictive outcome and retaining the original meaning of the features after reduction. One of the successful techniques for feature selection from datasets is the rough set theory (RST). This 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....

2000
Luigi Galavotti Fabrizio Sebastiani Maria Simi

We tackle two different problems of text categorization (TC), namely feature selection and classifier induction. Feature selection (FS) refers to the activity of selecting, from the set of r distinct features (i.e. words) occurring in the collection, the subset of r′ r features that are most useful for compactly representing the meaning of the documents. We propose a novel FS technique, based o...

Journal: :iranian journal of basic medical sciences 0
shokoufeh aalaei department of medical informatics, school of medicine, mashhad university of medical sciences, mashhad, iran hadi shahraki department of electrical engineering, faculty of engineering, university of birjand, birjand, iran alireza rowhanimanesh robotics laboratory, department of electrical engineering, university of neyshabur, neyshabur, iran saeid eslami department of medical informatics, school of medicine, mashhad university of medical sciences, mashhad, iran pharmaceutical research center, school of pharmacy, mashhad university of medical sciences, mashhad, iran department of medical informatics, academic medical center, amsterdam, the netherlands

objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

2012
Mani Abedini Michael Kirley Raymond Chiong

XCS, a Genetic Based Machine Learning model that combines reinforcement learning with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules, has been used successfully for many classification tasks. However, like many other machine learning algorithms, XCS becomes less effective when it is applied to high-dimensional data sets. In this paper, we pre...

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