High-dimensional feature selection for genomic datasets

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

متن کامل

Improved PSO for Feature Selection on High-Dimensional Datasets

Classification on high-dimensional (i.e. thousands of dimensions) data typically requires feature selection (FS) as a pre-processing step to reduce the dimensionality. However, FS is a challenging task even on datasets with hundreds of features. This paper proposes a new particle swarm optimisation (PSO) based FS approach to classification problems with thousands or tens of thousands of feature...

متن کامل

Feature selection for high-dimensional genomic microarray data

We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami’s (1996) Markov Blanket filter, to decide on particular featur...

متن کامل

Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

متن کامل

Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

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 ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Knowledge-Based Systems

سال: 2020

ISSN: 0950-7051

DOI: 10.1016/j.knosys.2020.106370