Cost - sensitive feature reduction applied to
نویسندگان
چکیده
This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the eeciency of learning. In addition, cost-sensitive feature elimination can be eeective for reducing costs of induced hypotheses.
منابع مشابه
Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...
متن کاملExperimental and numerical study of delamination detection in a WGF/epoxy composite plate using ultrasonic guided waves and signal processing tools
Reliable damage detection is one of the most critical tasks in composite plate structures. Ultrasonic guided waves are acknowledged as an effective way of structural health mo...
متن کاملCredit Card Fraud Detection using Data mining and Statistical Methods
Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...
متن کاملCost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm
This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant f...
متن کاملCost-Sensitive Feature Selection of Numeric Data with Measurement Errors
Feature selection is an essential process in datamining applications since it reduces amodel’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric datawithmeasurement errors.Themajor contributions of this paper are fourfold. First, a newdatamodel is built to address test...
متن کامل