Shunkai Fu

Fast Markov blanket discovery algorithm via local learning within single pass

Learning of Markov blanket (MB) can be regarded as an optimal solution to the feature selection problem. In this paper, an efficient and effective framework is suggested for learning MB. Firstly, we propose a novel algorithm, called Iterative Parent-Child based search of MB (IPC-MB), to induce MB without having to learn a whole Bayesian network first. It is proved correct, and is demonstrated to be more efficient than the current state of the art, PCMB, by requiring much fewer conditional independence (CI) tests. We show how to construct an AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. We conclude that IPC-MB plus AD-tree appears a very attractive solution in very large applications.

Tradeoff analysis of different Markov blanket local learning approaches

Discovering the Markov blanket of a given variable can be viewed as a solution for optimal feature subset selection. Since 1996, several algorithms have been proposed to do local search of the Markov blanket, and they are proved to be much more efficient than the traditional approach where the whole Bayesian Network has to be learned first. In this paper, we compare those known published algorithms, including KS, GS, IAMB and its variants, PCMB, and one newly proposed called BFMB. We analyze the theoretical basis and practical values of each algorithm with the aim that it will help applicants to determine which ones to take in their specific scenarios.

Local learning algorithm for markov blanket discovery

Learning of Markov blanket can be regarded as an optimal solution to the feature selection problem. In this paper, we propose a local learning algorithm, called Breadth-First search of MB (BFMB), to induce Markov blanket (MB) without having to learn a Bayesian network first. It is demonstrated as (1) easy to understand and prove to be sound in theory; (2) data efficient by making full use of the knowledge of underlying topology of MB; (3) fast by relying on fewer data passes and conditional independent test than other approaches; (4) scalable to thousands of variables due local learning. Empirical results on BFMB, along with known Iterative Association Markov blanket (IAMB) and Parents and Children based Markov boundary (PCMB), show that (i) BFMB significantly outperforms IAMB in measures of data efficiency and accuracy of discovery given the same amount of instances available (ii) BFMB inherits all the merits of PCMB, but reaches higher accuracy level using only around 20% and 60% of the number of data passes and conditional tests, respectively, used by PCMB.

Query recommendation and its usefulness evaluation on mobile search engine

In this paper, we study the role of query recommendation on mobile search engine. We start with the discussion of the role of query recommendation in modern search engines. Secondly, a mobile search engine, Roboo® (http://wap.roboo.com), is introduced and we discuss the need for query recommendation over mobile search engine. Thirdly, the query recommendation solution working on Roboo® is introduced in detail, including the models, how they are constructed and how they operate online. Finally, we demonstrate the benefits of query recommendation brought on Roboo® based on the analysis of real search log data collected since its release online in August, 2008. Considering the scarcity of scientific publications about the practical values of query recommendation on commercial mobile search engine, this paper should represent an interesting and useful reference for both academic and industrial colleagues.

Cross-Channel Query Recommendation on Commercial Mobile Search Engine: Why, How and Empirical Evaluation

Mobile search not only inherits some features of traditional search on PC, but also has many of its own special characteristics. In this paper, we firstly share some unique features about mobile search and discuss why vertical search is preferred. Providing multiple vertical searches is proved to be convenient to users but causes some minor problem as well. This plays as the initiative for us to propose cross-channel query recommendation. Secondly, we briefly introduce how to realize the cross-channel recommendation effectively and efficiently online. Finally, we analyze the performance of the proposed method from three different but related metrics: expected effect, off-line evaluation and on-line evaluation. All three studies together indicate that the proposed cross-channel recommendation is quite useful. Being the first study about query recommendation on mobile search, it is believed that the findings, proposed solution and collected feedback as presented here will be beneficial to both researchers and industry companies while considering how to provide better mobile search service.

Refinement of a Q-matrix with an Ensemble Technique Based on Multi-label Classification Algorithms

There are numerous algorithms and tools to help an expert map exercises and tasks to underlying skills. The last decade has witnessed a wealth of data driven approaches aiming to refine expert-defined mappings of tasks to skill. This refinement can be seen as a classification problem: for each possible mapping of task to skill, the classifier has to decide whether the expert’s advice is correct, or incorrect. Whereas most algorithms are working at the level of individual mappings, we introduce an approach based on a multi-label classification algorithm that is trained on the mapping of a task to all skills simultaneously. The approach is shown to outperform the existing task to skill mapping refinement techniques.

Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier

Multi-dimensional classification (MDC) aims at finding a function that assigns a vector of class values to a vector of observed features. Multi-dimensional Bayesian network classifier (MBNC) was devised for MDC in 2006, but with restricted structure. By removing the constraints, an undocumented model called general multi-dimensional Bayesian network classifier (GMBNC) is proposed in this article, along with an exact induction algorithm which is able to recover the GMBNC by local search, without having to learn the whole BN first. We prove its soundness, and conduct experimental studies to verify its effectiveness and efficiency. The larger is the problem, the more saving by IPC-GMBNC versus conventional approach (global structure learning by PC algorithm), e.g. given an example network with 200 nodes, around 99% saving is achieved.