Automatic Glaucoma Diagnosis with mRMR-based Feature Selection
نویسندگان
چکیده
منابع مشابه
Automatic Sleep Arousal Detection based on C-ELM and MRMR feature selection
Sleep arousals are sudden awakenings from sleep which can be identified as an abrupt shift in EEG frequency and can be manually scored from various physiological signals by sleep experts. Frequent sleep arousals can degrade sleep quality, result in sleep fragmentation and lead to daytime sleepiness. Visual inspection of arousal events from PSG recordings is cumbersome, and manual scoring result...
متن کاملPrediction of Protein Domain with mRMR Feature Selection and Analysis
The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many effor...
متن کاملmRMRe: an R package for parallelized mRMR ensemble feature selection
MOTIVATION Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature sp...
متن کاملFeature Selection Based on Genetic Algorithm in the Diagnosis of Autism Disorder by fMRI
Background: Autism Spectrum Disorder (ASD) occurs based on the continuous deficit in a person’s verbal skills, visual, auditory, touch, and social behavior. Over the last two decades, one of the most important approaches in studying brain functions in autistic persons is using functional Magnetic Resonance Imaging (fMRI). Objectives: It is common to use all brain regions in functional extracti...
متن کاملFeature Based Cut Detection with Automatic Threshold Selection
There has been much work concentrated on creating accurate shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. In this work we present a scheme based on stable feature tracking for inter frame differencing. Furthermore, we present a method to stabilize the differences and automatically detect a global threshol...
متن کاملذخیره 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: Journal of Biometrics & Biostatistics
سال: 2012
ISSN: 2155-6180
DOI: 10.4172/2155-6180.s7-008