U.S. NEW ENGLAND GROUNDFISH MANAGEMENT UNDER THE MAGNUSON-STEVENS FISHERY CONSERVATION AND MANAGEMENT ACT

Resource /Energy Economics and Policy,

Visual and semantic interpretability of projections of high dimensional data for classification tasks

A number of visual quality measures have been introduced in visual analytics literature in order to automatically select the best views of high dimensional data from a large number of candidate data projections. These methods generally concentrate on the interpretability of the visualization and pay little attention to the interpretability of the projection axes. In this paper, we argue that interpretability of the visualizations and the feature transformation functions are both crucial for visual exploration of high dimensional labeled data. We present a two-part user study to examine these two related but orthogonal aspects of interpretability. We first study how humans judge the quality of 2D scatterplots of various datasets with varying number of classes and provide comparisons with ten automated measures, including a number of visual quality measures and related measures from various machine learning fields. We then investigate how the user perception on interpretability of mathematical expressions relate to various automated measures of complexity that can be used to characterize data projection functions. We conclude with a discussion of how automated measures of visual and semantic interpretability of data projections can be used together for exploratory analysis in classification tasks.Comment: Longer version of the VAST 2011 poster. http://dx.doi.org/10.1109/VAST.2011.610247

Augmenting the Kappa Statistic to Determine Interannotator Reliability for Multiply Labeled Data Points

This paper describes a method for evaluating interannotator reliability in an email corpus annotated for type (e.g., question, answer, social chat) when annotators are allowed to assign multiple labels to a message. An augmentation is proposed to Cohen's kappa statistic which permits all data to be included in the reliability measure and which further permits the identification of more or less reliably annotated data points

On the Correlation between Energy and Pitch Accent in Read English Speech

In this paper, we describe a set of experiments that examine the correlation between energy and pitch accent. We tested the discriminative power of the energy component of frequency sub- bands with a variety of frequencies and bandwidths on read speech spoken by four native speakers of Standard American English, us- ing an analysis by classification approach. We found that the frequency region most robust to speaker differences is between 2 and 20 bark. Across all speakers, using only energy features we were able to predict pitch accent in read speech with accuracy of 81.9%

V-Measure: A conditional entropy-based external cluster evaluation

We present V-measure, an external entropy-based cluster evaluation measure. Vmeasure provides an elegant solution to many problems that affect previously defined cluster evaluation measures including 1) dependence on clustering algorithm or data set, 2) the “problem of matching”, where the clustering of only a portion of data points are evaluated and 3) accurate evaluation and combination of two desirable aspects of clustering, homogeneity and completeness. We compare V-measure to a number of popular cluster evaluation measures and demonstrate that it satisfies several desirable properties of clustering solutions, using simulated clustering results. Finally, we use V-measure to evaluate two clustering tasks: document clustering and pitch accent type clustering