Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm

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

Feature Selection by Kernelized Fuzzy Rough Sets for Transient Stability Assessment Based on Gaussian Process

Feature selection of input features is the key issue for pattern recognition-based transient stability assessment (TSA) methods. Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features are firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets...

متن کامل

A hybrid filter-based feature selection method via hesitant fuzzy and rough sets concepts

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...

متن کامل

Feature subset selection based on fuzzy neighborhood rough sets

Rough set theory has been extensively discussed in machine learning and pattern recognition. It provides us another important theoretical tool for feature selection. In this paper, we construct a novel rough set model for feature subset selection. First, we define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. A parameterized fuzzy relation is introduced to character...

متن کامل

Combining rough and fuzzy sets for feature selection

Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks th...

متن کامل

Soft fuzzy rough sets for robust feature evaluation and selection

The fuzzy dependency function proposed in the fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that this function is not robust to noisy information in this paper. As datasets in real-world applications are usually contaminated by noise, robustness of data analysis models is very important in practice. In this work, we develop a new model of fu...

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: International Journal of Electrical Power & Energy Systems

سال: 2015

ISSN: 0142-0615

DOI: 10.1016/j.ijepes.2014.07.070