Feature Selection for Classification Using an Ant System Approach
نویسنده
چکیده
Many applications such as pattern recognition and data mining require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant, redundant or noisy features while keeping the most informative ones. In this paper, an ant system approach for solving feature selection for classification is presented. The results we got are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets.
منابع مشابه
Fuzzy-rough Information Gain Ratio Approach to Filter-wrapper Feature Selection
Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-wrapper feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modifie...
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 کاملA hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection
A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...
متن کاملA Novel Approach to Feature Selection Using PageRank algorithm for Web Page Classification
In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analy...
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 کاملDetermining Effective Features for Face Detection Using a Hybrid Feature Approach
Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...
متن کامل