NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Eliciting and analyzing expert judgment: a practical guide
Eliciting and analyzing expert judgment: a practical guide
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Decision trees for ordinal classification
Intelligent Data Analysis
Nonparametric Monotone Classification with MOCA
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Ordinal classification with decision rules
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Preference Learning
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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In many ranking problems, common sense dictates that the rank assigned to an instance should be increasing (or decreasing) in one or more of the attributes describing it. Consider, for example, the problem of ranking documents with respect to their relevance to a particular query. Typical attributes are counts of query terms in the abstract or title of the document, so it is natural to postulate the existence of an increasing relationship between these counts and document relevance. Such relations between attributes and rank are called monotone. In this paper we present a new algorithm for instance ranking called mira which learns a monotone ranking function from a set of labelled training examples. Monotonicity is enforced by applying the isotonic regression to the training sample, together with an interpolation scheme to rank new data points. This is combined with logistic regression in an attempt to remove unwanted rank equalities. Through experiments we show that mira produces ranking functions having predictive performance comparable to that of a state-of-the-art instance ranking algorithm. This makes mira a valuable alternative when monotonicity is desired or mandatory.