14 research outputs found
A critical view on invexity
The aim of this note is to emphasize the fact that in many papers on invexity
published in prestigious journals there are not clear definitions, trivial or
not clear statements and wrong proofs. We also point out the unprofessional way
of answering readers' questions by some authors. We think that this is caused
mainly by the lack of criticism of the invexity communityComment: The paper was submitted to JOTA in December 2007 and practically
accepted by the AE handling it in March 2008. Being a critical paper, the EiC
asked the authors of the criticised articles to say their opinion. With the
change of the EiC's, apparently the paper was not transmitted to the new Ei
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i.e., the fraction of a population that
belongs to a certain class, is a very useful tool in data analytics and
learning, and finds applications in many domains such as sentiment analysis,
epidemiology, etc. For example, in sentiment analysis, the objective is often
not to estimate whether a specific text conveys a positive or a negative
sentiment, but rather estimate the overall distribution of positive and
negative sentiments during an event window. A popular way of performing the
above task, often dubbed quantification, is to use supervised learning to train
a prevalence estimator from labeled data.
Contemporary literature cites several performance measures used to measure
the success of such prevalence estimators. In this paper we propose the first
online stochastic algorithms for directly optimizing these
quantification-specific performance measures. We also provide algorithms that
optimize hybrid performance measures that seek to balance quantification and
classification performance. Our algorithms present a significant advancement in
the theory of multivariate optimization and we show, by a rigorous theoretical
analysis, that they exhibit optimal convergence. We also report extensive
experiments on benchmark and real data sets which demonstrate that our methods
significantly outperform existing optimization techniques used for these
performance measures.Comment: 26 pages, 6 figures. A short version of this manuscript will appear
in the proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining, KDD 201