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Recently published journal papers--June 2011 (Expired)

The Department of Computer Science is pleased to announce the publication of journal papers by members of our department:

Six journal papers by faculty member Dr. Yiyu Yao:

  1. Yao, Y.Y., Two semantic issues in a probabilistic rough set model, Fundamenta Informaticae, Vol. 108, No. 3-4, 249-265, 2011.

  2. Yao, Y.Y., The superiority of three-way decisions in probabilistic rough set models, Information Sciences, Vol. 181, No. 6, 1080-1096, 2011.

  3. Grzymala-Busse, J.W., and Yao, Y.Y., Probabilistic rule induction with the LERS data mining system, International Journal of Intelligent Systems, Vol. 26, No. 6, 518-539, 2011.

  4. Liu, D., Yao, Y.Y., and Li, T.R., Three-way investment decisions with decision-theoretic rough sets, International Journal of Computational Intelligence Systems, Vol.4, No. 1, 66-74, 2011.

  5. Zhao, Y., Wong, S.K.M. and Yao, Y.Y., A Note on Attribute Reduction in the Decision-Theoretic Rough Set Model, LNCS Transactions on Rough Sets XIII, LNCS 6499, pp. 260-275, 2011.

  6. Zeng, Y., Zhong, N., Wang, Y., Qin, Y.L., Huang, Z.S., Zhou, H.Y., Yao, Y.Y., and van Harmelen, F., User-centric query refinement and processing using granularity-based strategies, Knowledge and Information Systems, Vol. 27, No. 3, 419-450, 2011.


"Models of Cooperative Teaching and Learning," by Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich, Journal of Machine Learning Research, 12:349-384, 2011.  Dr. Zilles is a faculty member in our department.  The Journal of Machine Learning Research is the most cited Machine Learning journal in the world.


While most supervised machine learning models assume that training examples are sampled at random or adversarially, this article is concerned with models of learning from a cooperative teacher that selects "helpful" training examples. The number of training examples a learner needs for identifying a concept in a given class C of possible target concepts (sample complexity of C) is lower in models assuming such teachers, that is, "helpful" examples can speed up the learning process. The problem of how a teacher and a learner can cooperate in order to reduce the sample complexity, yet without using "coding tricks", has been widely addressed. Nevertheless, the resulting teaching and learning protocols do not seem to make the teacher select intuitively "helpful" examples. The two models introduced in this paper are built on what we call subset teaching sets and recursive teaching sets. They extend previous models of teaching by letting both the teacher and the learner exploit knowing that the partner is cooperative. For this purpose, we introduce a new notion of "coding trick"/"collusion".  We show how both resulting sample complexity measures (the subset teaching dimension and the recursive teaching dimension) can be arbitrarily lower than the classic teaching dimension and known variants thereof, without using coding tricks. For instance, monomials can be taught with only two examples independent of the number of variables. The subset teaching dimension turns out to be nonmonotonic with respect to subclasses of concept classes. We discuss why this nonmonotonicity might be inherent in many interesting cooperative teaching and learning scenarios.

For more information, see:


``Generation and Interpretation of Temporal Decision Rules,'' by Kamran Karimi and Howard J. Hamilton, International Journal of Computer Information Systems and Industrial Management Applications, Volume 3, 2011, 314-323.


We present a solution to the problem of understanding a system that produces a sequence of temporally ordered observations. Our solution is based on generating, interpreting, and visualizing a set of temporal decision rules. A temporal decision rule is a decision rule that can be used to predict or retrodict the value of a decision attribute, using condition attributes that are observed at different times than the decision attribute. A rule set, consisting of a set of temporal decision rules with the same decision attribute, can be interpreted by our Temporal Investigation Method for Enregistered Record Sequences (TIMERS) to signify a possibly causal, an acausal, or an instantaneous relationship between the condition attributes and the decision attribute. We show the effectiveness of our method, by describing a number of experiments with both synthetic and real temporal data from a weather station.

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