A Maximal Margin Classification Algorithm Based on Data Field
نویسنده
چکیده
This paper puts forward a new maximal margin classification algorithm based on general data field (MMGDF). This method transforms the linear inseparable problem into finding the nearest points in the general data field (GDF). GDF is inspired by the physical field. Different dimensions represent the different properties. Not all attributes play a decisive role in the classification process. Therefore, how to find decisive data points and attributes is a critical issue. This research builds a general data field structure in high dimension data sets. The role of data point is expanded from local to global by GDF. We calculate the weights of data points and features by the potential value in data field space. We put it into practice. Experiments show that MM-GDF method is better than the exiting common methods.
منابع مشابه
A web portal for classification of expression data using maximal margin linear programming
The Maximal Margin (MAMA) linear programming classification algorithm has recently been proposed and tested for cancer classification based on expression data. It demonstrated sound performance on publicly available expression datasets. We developed a web interface to allow potential users easy access to the MAMA classification tool. Basic and advanced options provide flexibility in exploitatio...
متن کاملClassification of encrypted traffic for applications based on statistical features
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applicat...
متن کاملMargin Adaptive Risk Bounds for Classification Trees
Margin adaptive risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penalty used in the CART pruning algorithm is valid under margin condition. It is also show...
متن کاملA Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems
Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the pro...
متن کاملA second order cone programming approach for semi-supervised learning
Semi-supervised learning (SSL) involves the training of a decision rule from both labeled and unlabeled data. In this paper, we propose a novel SSL algorithm based on the multiple clusters per class assumption. The proposed algorithm consists of two stages. In the first stage, we aim to capture the local cluster structure of the training data by using the k-nearest-neighbor (kNN) algorithm to s...
متن کامل