3,924 research outputs found
Search Strategies for Binary Feature Selection for a Naive Bayes Classifier
We compare in this paper several feature selection methods for the Naive
Bayes Classifier (NBC) when the data under study are described by a large
number of redundant binary indicators. Wrapper approaches guided by the NBC
estimation of the classification error probability out-perform filter
approaches while retaining a reasonable computational cost
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
Feature Selection via Coalitional Game Theory
We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets
Combining similarity in time and space for training set formation under concept drift
Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications
Resampling methods for parameter-free and robust feature selection with mutual information
Combining the mutual information criterion with a forward feature selection
strategy offers a good trade-off between optimality of the selected feature
subset and computation time. However, it requires to set the parameter(s) of
the mutual information estimator and to determine when to halt the forward
procedure. These two choices are difficult to make because, as the
dimensionality of the subset increases, the estimation of the mutual
information becomes less and less reliable. This paper proposes to use
resampling methods, a K-fold cross-validation and the permutation test, to
address both issues. The resampling methods bring information about the
variance of the estimator, information which can then be used to automatically
set the parameter and to calculate a threshold to stop the forward procedure.
The procedure is illustrated on a synthetic dataset as well as on real-world
examples
Weka: A machine learning workbench for data mining
The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License
Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a
descriptive way, to measure the stochastic dependence of discrete random
variables. In order to address questions such as the reliability of the
empirical value, one must consider sample-to-population inferential approaches.
This paper deals with the distribution of mutual information, as obtained in a
Bayesian framework by a second-order Dirichlet prior distribution. The exact
analytical expression for the mean and an analytical approximation of the
variance are reported. Asymptotic approximations of the distribution are
proposed. The results are applied to the problem of selecting features for
incremental learning and classification of the naive Bayes classifier. A fast,
newly defined method is shown to outperform the traditional approach based on
empirical mutual information on a number of real data sets. Finally, a
theoretical development is reported that allows one to efficiently extend the
above methods to incomplete samples in an easy and effective way.Comment: 8 two-column page
Feature selection algorithms: a survey and experimental evaluation
In view of the substantial number of existing feature selection
algorithms, the need arises to count on criteria that
enables to adequately decide which algorithm to use in certain
situations. This work reviews several fundamental algorithms found in the
literature and assesses their performance in a controlled
scenario. A scoring measure ranks the algorithms by
taking into account the amount of relevance, irrelevance
and redundance on sample data sets. This measure computes the
degree of matching between the output given by the algorithm and the known
optimal solution. Sample size effects are also studied.Postprint (published version
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