In this paper a new method is presented to solve a series of multiclass object classification problems using Genetic Programming (GP). All component two-class subproblems of the multiclass problem are solved in a single run, using a multi-objective fitness function. Probabilistic methods are used, with each evolved program required to solve only one subproblem. Programs gain a fitness related t...

MOTIVATION Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve bi...

Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sublinearly with the number of possible classes. This implies that features should be shared by several classes. We describe and analyze the ShareBoost al...

Accurate classification of dialog acts (DAs) is important for many spoken language applications. Different methods have been proposed such as hidden Markov models (HMM), maximum entropy (Maxent), graphical models, and support vector machines (SVMs). In this paper, we investigate using SVMs for multiclass DA classification in the ICSI meeting corpus. We evaluate (1) representing DA tagging direc...

This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-nearest neighbor classi cation. Given a family of distance measures as input, AdaBoost is used to learn a weighted distance measure, that is a linear combination of the input measures. The proposed method can be seen both as a novel way to learn a distance measure from data, and as a novel way to ...

We develop a new multiclass classification method that reduces the multiclass problem to a single binary classifier (SBC). Our method constructs the binary problem by embedding smaller binary problems into a single space. A good embedding will allow for large margin classification. We show that the construction of such an embedding can be reduced to the task of learning linear combinations of k...

Los sistemas de recomendación se utilizan para realizar recomendaciones de ítems potencialmente interesantes para un usuario en variados dominios. Existe un gran número de dominios que sugieren la necesidad de proveer técnicas de personalización para grupos de usuarios y no sólo focalizarse en usuarios individuales. En este trabajo se presentan dos aplicaciones que implementan técnicas de gener...

Security in mobile ad-hoc network plays a strategic role to ensure high level of protection without any intrusions in computer networks. Most of the intrusions in mobile ad-hoc network are traced and detected by collecting traffic information and classified according to different classification algorithms. With individual traffic classifiers design, packet delay is expected to surely go up with...

Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confu...

A consistent loss function for multiclass classification is one such that for any source of labeled examples, any tuple of scoring functions that minimizes the expected loss will have classification accuracy close to that of the Bayes optimal classifier. While consistency has been proposed as a desirable property for multiclass loss functions, we give experimental and theoretical results exhibi...