** **

**Instance Selection**

**with Similarity-based, Evolutionary and Embedded Methods**

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**Mirosław Kordos**

**2009-2019**

** Purpose of Instance Selection**** **

Good data preprocessing, including data selection is frequently the most
important part of the prediction process, even more important than the optimal
model parameters. This is because the models can be only as good as the data
used for their learning. The list of data selection purposes begins with
reduction of the data size and noise elimination, which both improve the data
quality. They allow for achieving higher accuracy, shorter learning time and
lower complexity of predictive models as well as easier data interpretation.

Data selection comprises feature selection and instance selection. Much
more research has been done so far in feature selection. Since feature
selection and instance selection are equally important. To bridge the gap my
work focuses on instance selection and on the merge of instance and feature
selection. However, as instance selection and feature selection are dependent
on each other, in several places here feature selection is also considered,
usually in connection with instance selection, but little new research in the
feature selection is presented in my works.

**Part 1.**** Instance Selection
with Similarity-based Methods**

Similarity-based instance selection methods can be divided into noise filters
and condensation methods. In both cases they can be presented as a
function

**Weight(x) = F(Properties(neighbors(x)),
Model(x, neighbors(x)))**

where:

·
Weight(x) is a value that indicates the importance of
the vector x (how much vector x is required or desired)

·
Properties of the neighbors of x can express local diversity
of the data (e.g. standard deviation) or the trends in the data variability.

·
Model(x, neighbors(x)) is some predictor (classifier)
that learns on neighbors(x) and predicts x; that can be k-NN, neural network,
etc.

Then we set some threshold, and the instances with their weight below
the threshold get rejected. But they still can be kept in the training dataset
and their influence on the model can be multiplied by their weight. For example
if the model is a neural network, then the error the network makes on each
instance is multiplied by the instance weight, if the model is k-NN, then a
weighed voting takes place.

In most of the instance selection methods, the Model(.)
is based on some distance or neighborhood concept, as it is simple and
computationally efficient. For example in the ENN algorithm (which is a noise
filter),

k-NN is used to predict the class of each vector. It the real class of the
vector is different than the predicted class then the vector is marked for
removal, as it is considered to be noise. Finally all the marked vectors get
removed. In case of instance weighting, the weight can be proportional to the
percentage of the nearest neighbors that belong to the same class as the query
instance.

In regression problems there are no classes, but that can be solved at
least in two ways: by output attribute discretization and converting the
regression problem into multi-label classification problem or by replacing the
“same class” concept with some threshold distance between the real and the
predicted value of each vector.

Condensation methods work by rejecting these vectors, which do not
degrade the classification of their neighbors. But the concept used in weighing
and regression problems can be applied in analogous way.

Left: only the correctly classified instances that are close to
the decision boundary are required in classification (some exceptions apply).
Right: instances very similar and very different from their neighbors can be
removed in regression.

To illustrate the importance of properties of the neighborhood of a
given point let us use a regression problem example. If the standard deviation
of the neighbors of the point x is say 0.01 and the real value of the point
differs from the predicted value by 0.5 – then probably this point makes noise
and thus should be rejected. However, if the standard deviation is 10, then a
difference of 0.5 looks OK.

This research area comprises various aspects of similarity-based instance
selection, which were mentioned here, but particular instance selection
algorithms, the use of clustering to improve instance selection speed and
accuracy, ensemble methods in instance selection and the joined selection of
features and instances.

My contribution in this area includes adaptation of the instance
selection algorithms to regression problems, optimization of the Properties(Neighbors(x)) methods with weights reflecting the
attribute, instance, distance, density and gradient properties, enhancements of
ensemble methods in instance selection and concurrent instance and feature
selection.

**Part 2.**** Instance Selection
with Evolutionary Methods**** **

Evolutionary methods of instance selection optimize the following
function:

** **

**Fitness(****dataset) =
F(accuracy, compression)**

The first difference is that they optimize globally the whole training dataset,
unlike the similarity-based methods, which deal with each instance separately. The
optimization can be either single-objective or multi-objective, binary or
real-value. In a single objective optimization we set an
α parameter and optimize the function:

**Fitness(****dataset) = **α** accuracy+ (1-** α) **compression**

We can also make the compression over features and instances in one
optimization, then

**compression**** = instanceCompression * featureCompression**

Evolutionary or genetic algorithms use population of solutions, where
each instance is encoded into one position in the chromosome and the typical
genetic operators as fitness-based selection, crossover and mutation. However,
the similarity-based methods can also use an equivalent of population – ensembles. The
relation between ensembles and populations is also worth analyzing.

The second part is dedicated to various evolutionary methods of instance
selection and instance weighting (in this case vectors are represented as real numbers
between 0 and 1) and their properties and optimization as well as joining the
similarity-based and evolutionary approach. It also deals with concurrent instance and
feature selection. In that case the chromosome can consist of the instance part
and the feature part, or two optimizations can be performed: one for instances
and one for features and they can get synchronized at
each iteration.

Features (green) and instances (orange) encoded in the chromosome.

My contribution to that part includes solutions and improvements in
speed and accuracy of instance weighting and selection by optimization of
genetic and evolutionary algorithm process and its parameters. Different
parameters best optimize the speed for CPU and for GPU calculations. Other ways
to reduce the computational cost include the similarity-based methods and ensembles
merged into in the evolutionary process. The evolutionary instance selection
can be much less costly than the product of the number of fitness function
evaluations by the cost of a single instance selection run, as much of the
calculations can be simplified. For example, the full k-NN algorithm can be run
only once at the beginning of optimization and the ordered distances can be
intelligently cached.

It sometimes takes longer than similarity-based instance selection, but not always and
the obtained results are much better, especially for classification tasks
allowing for frequently much stronger compression than that of DROP3 or
ENN+IB3 (which are considered one of the best similarity-based methods) and slightly
higher prediction accuracy at the same time.

** **

Left: comparison of solution quality of instance selection methods.
Right: population size optimization for CPU implementation (for GPU the optimal
size can be much bigger and, unlike in CPU, it depends on the way fitness
function is evaluated).

In multi-objective optimization the aim is to find the Pareto-front of
non-dominated solutions, what corresponds to a simultaneous optimization for
various α parameters. Recently we have obtained groundbreaking results with a modified version
of NGSA-II algorithm applied to instance selection for regression problems. The method allows for obtaining a pool of solutions achieving good performance in terms of Pareto front of the minimization of rmse and maximization of compression and choosing the desired solution from the front. Different Pareto fronts can be obtained depending on several parameters of the instance selection process.

**Part 3.**** Instance Selection
with Embedded Methods**

The basic idea behind instance selection embedded into neural networks
is that the instance properties are reflected by the error the trained network
makes on that instance. If the error is very high then the instance can be
considered noise and should be removed. However, it cannot be removed at the
beginning of the network training, because at that time all vectors produce
random errors, so the removal must be gradual and frequently better results can
be obtained with instance weighting instead of removing. On contrary if the
error is very low, the instance is probable situated far from decision boundary
in the classification problem and also can be removed as it does not carry any
useful information.

Left: bump functions are very efficient for instance weighting in
regression. Right: only the instances on
the sigmoid slope matters in classification.

Different methods of instance detecting, weighting and removing have
been developed, including merging the similarity-based methods with the neural
network–based ones and joining instance selection with feature selection. Some
of the methods required creating new network learning algorithms, as
gradient-based method could not be applied. So also the learning algorithms
developed by us will be presented. Next, the choice of particular noise removal
method should be adjusted to the character of the data.

How particular methods perform depending on the noise level in the
training data.

Feature selection can be done by analysis of the weights in the trained
network and then by removing these features, which propagates to the output
week signals. Also feature selection can be joined with instance selection and
this is also considered.

Feature selection in this context is closely related to decompositional rule extraction, as a similar analysis of
signal propagation through the network weights is performed. The idea of
logical rule extraction is to make the extracted rules enough simple to be
understood by humans. This usually leads to some discretization of the input
space in classification tasks. In regression tasks there are more ways to solve
the problem.

There is also a connection between rule extraction and instance
selection. Adjusting the network to rule extraction frequently also helps
filtering out noisy instances and thus makes the network more noise-robust. The
trade-off between the rule simplicity and the network accuracy can usually be
addressed by incremental network structure, starting from the most simple and
general areas, leading to the most accurate and complex one, so the user can
make the analysis and any desired level of details.

Also here, as in similarity-based data selection, ensembles can be applied.
Finally a sketch of how the embedded methods can be applied into other models
will be presented.

**Summary**

The state-of-the-art instance selection methods and joined instance
selection with feature selection are a very promising research area with
practical applications. The solutions allow for improvements of the training
data quality and as a consequence to improve the prediction process by allowing
for higher accuracy and shorter training and prediction time and enable us to
better understand the data properties and the properties of the processes
represented by the data.

The software, datasets, detailed results of experiments, papers, links
to related resources, and other information are successively being placed on my
web page.