7,802 research outputs found
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
Perhaps the most straightforward classifier in the arsenal or machine
learning techniques is the Nearest Neighbour Classifier -- classification is
achieved by identifying the nearest neighbours to a query example and using
those neighbours to determine the class of the query. This approach to
classification is of particular importance because issues of poor run-time
performance is not such a problem these days with the computational power that
is available. This paper presents an overview of techniques for Nearest
Neighbour classification focusing on; mechanisms for assessing similarity
(distance), computational issues in identifying nearest neighbours and
mechanisms for reducing the dimension of the data.
This paper is the second edition of a paper previously published as a
technical report. Sections on similarity measures for time-series, retrieval
speed-up and intrinsic dimensionality have been added. An Appendix is included
providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN
Coupling different methods for overcoming the class imbalance problem
Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical.
Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches.
To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature.
Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357
Taking the bite out of automated naming of characters in TV video
We investigate the problem of automatically labelling appearances of characters in TV or film material
with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying
when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”
MIRACLE’s Naive Approach to Medical Images Annotation
One of the proposed tasks of the ImageCLEF 2005 campaign has been an Automatic Annotation Task. The objective is to provide the classification of a given set of 1,000 previously unseen medical (radiological) images according to 57 predefined categories covering different medical pathologies. 9,000 classified training images are given which can be used in any way to train a classifier. The Automatic Annotation task uses no textual information, but image-content information only. This paper describes our participation in the automatic annotation task of ImageCLEF 2005
Disentangling Factors of Variation by Mixing Them
We propose an approach to learn image representations that consist of
disentangled factors of variation without exploiting any manual labeling or
data domain knowledge. A factor of variation corresponds to an image attribute
that can be discerned consistently across a set of images, such as the pose or
color of objects. Our disentangled representation consists of a concatenation
of feature chunks, each chunk representing a factor of variation. It supports
applications such as transferring attributes from one image to another, by
simply mixing and unmixing feature chunks, and classification or retrieval
based on one or several attributes, by considering a user-specified subset of
feature chunks. We learn our representation without any labeling or knowledge
of the data domain, using an autoencoder architecture with two novel training
objectives: first, we propose an invariance objective to encourage that
encoding of each attribute, and decoding of each chunk, are invariant to
changes in other attributes and chunks, respectively; second, we include a
classification objective, which ensures that each chunk corresponds to a
consistently discernible attribute in the represented image, hence avoiding
degenerate feature mappings where some chunks are completely ignored. We
demonstrate the effectiveness of our approach on the MNIST, Sprites, and CelebA
datasets.Comment: CVPR 201
Forgetting Exceptions is Harmful in Language Learning
We show that in language learning, contrary to received wisdom, keeping
exceptional training instances in memory can be beneficial for generalization
accuracy. We investigate this phenomenon empirically on a selection of
benchmark natural language processing tasks: grapheme-to-phoneme conversion,
part-of-speech tagging, prepositional-phrase attachment, and base noun phrase
chunking. In a first series of experiments we combine memory-based learning
with training set editing techniques, in which instances are edited based on
their typicality and class prediction strength. Results show that editing
exceptional instances (with low typicality or low class prediction strength)
tends to harm generalization accuracy. In a second series of experiments we
compare memory-based learning and decision-tree learning methods on the same
selection of tasks, and find that decision-tree learning often performs worse
than memory-based learning. Moreover, the decrease in performance can be linked
to the degree of abstraction from exceptions (i.e., pruning or eagerness). We
provide explanations for both results in terms of the properties of the natural
language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex
styles. Pre-print version of article to appear in Machine Learning 11:1-3,
Special Issue on Natural Language Learning. Figures on page 22 slightly
compressed to avoid page overloa
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