38761790
Social Choice For Data Fusion
Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.
28798884
Linear and Integer Programming Techniques for Cooperative Games
Let Γ = (N, v) be a cooperative game with the player set N and value function v : 2N → R. A solution of the game is in the core if no subset of players could gain advantage by breaking away from the grand coalition of all players. This paper surveys theoretical results on the cores for some cooperative game models. These results proved that the linear program duality characterization of the core is a very powerful tool. We will focus on linear and integer programming techniques applied in this area.
14172606
A probabilistic model for mining implicit 'chemical compound-gene' relations from literature
MOTIVATION
The importance of chemical compounds has been emphasized more in molecular biology, and 'chemical genomics' has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit 'chemical compound and gene' relations from the co-occurrence in the literature.


RESULTS
We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound-gene, gene-gene and compound-compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound-compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound-gene (more specifically, drug-gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.
36453049
Parallel Models and Job Characterization for System Scheduling
In this work, we study job characterization in multi-programmed multiprocessor system by taking into consideration the parallel job models. We first introduce an example to illustrate the issues involved. Then we focus on two popular system scheduling policies: round-robin for single processor systems, and equi-partition for multiprocessor systems. We analytically study effect of job parallelization on the overall performance of the system, and also present simulation results. Through these studies, we discuss scheduling policies of parallel jobs in relation to the ratio of number of jobs and the number of processors in the multiprocessor system, and propose new paradigm for parallel algorithm designs.
2853127
Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine
Metasearch Engine is popular fo r facilitating users’ queries over multiple search engines and increasing the coverage of the WWW. How to rank the merged results becomes crucial f o r the success of metasearch engines. Many current metasearch engines have poor precision, f o r one or more of selected source search engine returns irrelevant results. On the other hand, users with different interests may prefer distinct ranking order even for the same query. In this work, we try to use online relevance feedback to improve precision of the search results. At the same time, Users’ preferences are recorded during the process of feedback f o r future ranking. Our elementary experiment shows that it is effective in improving precision of the metasearch engine.
5743908
A Probabilistic Model for Clustering Text Documents with Multiple Fields
We address the problem of clustering documents with multiple fields, such as scientific literature with the distinct fields: title, abstract, keywords, main text and references. By taking into consideration of the distinct word distributions of each field, we propose a new probabilistic model, Field Independent Clustering Model (FICM), for clustering documents with multiple fields. The benefits of FICM come not only from integrating the discrimination abilities of each field but also from the power of selecting the most suitable component probabilistic model for each field. We examined the performance of FICM on the problem of clustering biomedical documents with three fields (title, abstract and MeSH). From the genomics track data of TREC 2004 and TREC 2005, we randomly generated 60 datasets where the number of classes in each dataset ranged from 3 to 12. By applying the appropriate configuration of generative models for each field, FICM outperformed a classical multinomial model in 59 out of the total 60 datasets, of which 47 were statistically significant at the 95% level, and FICM also outperformed a multivariate Bernoulli model in 52 out of the total 60 datasets, of which 36 were statistically significant at the 95% level.
7400363
Empirical Studies on Multi-label Classification
In classic pattern recognition problems, classes are mutually exclusive by definition. However, in many applications, it is quite natural that some instances belong to multiple classes at the same time. In other words, these applications are multi-labeled, classes are overlapped by definition and each instance may be associated to multiple classes. In this paper, we present a comparative study on various multi-label approaches using both gene and scene data sets. We expect our research efforts provide useful insights on the relationships among various classifiers as well as various evaluation measures and shed lights on future research. Although there is no clear winner across various performance measures, SVM binary and multi-label ADTree perform better than the others on most counts. We then propose a meta-learning approach by combining SVM binary and ADTree. Our experiments demonstrate that the combined method can take the advantages of the single approaches
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.
12277810
Fuzzy Cultural Algorithms with Evolutionary Programming
Cultural Algorithms represent computational models based upon principles of cultural evolution [1]. They consist of a population component, a belief space containing knowledge compiled from individual experience, and a communication protocol which controls the interaction between two components. This protocol consists of an acceptance function and an influence function. The acceptance function determines how individuals can affect the knowledge contained in the belief space. The influence function determines how that knowledge can affect the individuals in the population.
17718282
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversible jump and diffusion Markov chain dynamics and thus achieves globally optimal solutions under the Bayesian statistical framework. Thus it realizes the cue integration and multiple model switching. Fourthly, it adopts two techniques to improve the speed of the Markov chain search: One is a coarse-to-fine strategy and the other are data driven techniques such as edge detection and clustering. The data driven methods provide important information for narrowing the search spaces in a probabilistic fashion. We apply the algorithm to two data sets and the experiments demonstrate robust and satisfactory results on both. Based on the segmentation results, we extend the reconstruction of surfaces behind occlusions to fill in the occluded parts.
15435799
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel technique that captures the structure and temporal dynamics of blog communities. In our framework, a community is a set of blogs that communicate with each other triggered by some events (such as a news article). The community is represented by its structure and temporal dynamics: a community graph indicates how often one blog communicates with another, and a community intensity indicates the activity level of the community that varies over time. Our method, community factorization, extracts such communities from the blogosphere, where the communication among blogs is observed as a set of subgraphs (i.e., threads of discussion). This community extraction is formulated as a factorization problem in the framework of constrained optimization, in which the objective is to best explain the observed interactions in the blogosphere over time. We further provide a scalable algorithm for computing solutions to the constrained optimization problems. Extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods.