13917211
Web page classification with heterogeneous data fusion
Web pages are more than text and they contain much contextual and structural information, e.g., the title, the meta data, the anchor text,etc., each of which can be seen as a data source or are presentation. Due to the different dimensionality and different representing forms of these heterogeneous data sources, simply putting them together would not greatly enhance the classification performance. We observe that via a kernel function, different dimensions and types of data sources can be represented into acommon format of kernel matrix, which can be seen as a generalized similarity measure between a pair of web pages. In this sense, a kernel learning approach is employed to fuse these heterogeneous data sources. The experimental results on a collection of the ODP database validate the advantages of the proposed method over traditional methods based on any single data source and the uniformly weighted combination of them.
1744668
Feature Selection with Particle Swarms
Feature selection is widely used to reduce dimension and remove irrelevant features. In this paper, particle swarm optimization is employed to select feature subset for classification task and train RBF neural network simultaneously. One advantage is that both the number of features and neural network configuration are encoded into particles, and in each iteration of PSO there is no iterative neural network training sub-algorithm. Another is that the fitness function considers three factors: mean squared error between neural network outputs and desired outputs, the complexity of network and the number of features, which guarantees strong generalization ability of RBF network. Furthermore, our approach could select as small-sized feature subset as possible to satisfy high accuracy requirement with rational training time. Experimental results on four datasets show that this method is attractive.
20162029
Effective Feature Preprocessing for Time Series Forecasting
Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting, there is so far no systematic research to study and compare their performance. How to select effective techniques of feature preprocessing in a forecasting model remains a problem. In this paper, the authors conduct a comprehensive study of existing feature preprocessing techniques to evaluate their empirical performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time series forecasting models.
16217697
Large-sample performance of blind and Group-blind multiuser detectors: a perturbation perspective
In blind and group-blind multiuser detection, different detectors can be designed using either the sample data covariance matrix directly or its eigencomponents. Due to finite-sample effect in practice, their performance deviates from the corresponding optimum. A perturbation technique is developed rigorously and systematically to analyze those detectors in this work. Subject to the assumption that the first-order perturbation dominates, corresponding results can be applied to a practical system of a given sample size. In particular, performance of the following typical detectors is studied for either flat or estimated multipath channels: direct-matrix-inversion (DMI) blind minimum mean-square error (MMSE) detector, subspace blind MMSE detector, direct zero-forcing (ZF) detector, subspace ZF detector, and group-blind hybrid detector. Simulation examples further verify various analytical results.
29379871
A Fixed-Length Subset Genetic Algorithm for the p-Median Problem
In this paper, we review some classical recombination operations and devise new heuristic recombinations for the fixed-length subset. Our experimental results on the classical p-median problem indicate that our method is superior and very close to the optimal solution.
891216
Infinite Hidden Relational Models
In many cases it makes sense to model a relationship symmetrically, not implying any particular directionality. Consider the classical example of a recommendation system where the rating of an item by a user should symmetrically be dependent on the attributes of both the user and the item. The attributes of the (known) relationships are also relevant for predicting attributes of entities and for predicting attributes of new relations. In recommendation systems, the exploitation of relational attributes is often referred to as collaborative filtering. Again, in many applications one might prefer to model the collaborative effect in a symmetrical way. In this paper we present a relational model, which is completely symmetrical. The key innovation is that we introduce for each entity (or object) an infinite-dimensional latent variable as part of a Dirichlet process (DP) model. We discuss inference in the model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant process. We extend the Chinese restaurant process to be applicable to relational modeling. Our approach is evaluated in three applications. One is a recommendation system based on the MovieLens data set. The second application concerns the prediction of the function of yeast genes/proteins on the data set of KDD Cup 2001 using a multi-relational model. The third application involves a relational medical domain. The experimental results show that our model gives significantly improved estimates of attributes describing relationships or entities in complex relational models.
15768911
The Bottleneck Problem with Minimum Quantity Commitments
Given an edge-distance graph of a set of suppliers and clients, the bottleneck problem is to assign each client to a selected supplier minimizing their maximum distance. We introduce minimum quantity commitments to balance workloads of suppliers, provide it a 3-approximation algorithm, and study its generalizations.