Incremental Subgraph Feature Selection for Graph Classification
نویسندگان
چکیده
منابع مشابه
Discriminative Feature Selection for Uncertain Graph Classification
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presenc...
متن کاملFeature Selection for Classification
Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970’s to the present. It identifies four s...
متن کاملA Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization
Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...
متن کاملDynamic Feature Space and Incremental Feature Selection for the Classification of Textual Data Streams
Real world text classification applications are of special interest for the machine learning and data mining community, mainly because they introduce and combine a number of special difficulties. They deal with high dimensional, streaming, unstructured, and, in many occasions, concept drifting data. Another important peculiarity of streaming text, not adequately discussed in the relative litera...
متن کاملEnsemble 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 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: IEEE Transactions on Knowledge and Data Engineering
سال: 2017
ISSN: 1041-4347
DOI: 10.1109/tkde.2016.2616305