Tuning Smote
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Home Page: https://dl.acm.org/citation.cfm?id=3180197
ICSE'18: Tuning Smote
Home Page: https://dl.acm.org/citation.cfm?id=3180197
By keeping change logs of the most recently or frequently
changed files are the most probable source of future de-
fects [11], [21], [7]. They
Most of these implementations are provided in Scikit-Learn~\cite{pedregosa2011scikit} and used in our code.
K metrics combined with OO (object-oriented) metrics perform better than all other metrics.
Weka: Total 20 attributes in each datasets. Datasets from top to bottom (high to low imbalance) . CFS attribute selection, and breadth first.
here's a light weight description. mote that point3 has to be changed for numeric attributes
2. DE scores each {\em pop}$_i$ according to various objective
scores $o$. In the case of our goal models, the objectives are $o_1$ the sum of the cost
of its decisions, $o_2$ the number of ignore edges, and the number of $o_3$ satisfied goals
and $o_4$ softgoals.
3. OPTIMIZE tries to each replace {\em pop}$_i$ with a mutant $q$
built by extrapolating between three other members of population $a,b,c$.
At probability $p_1$, for each decision $a_k \in a$, then
$m_k= a_k \vee (p_1 < \mathit{rand}() \wedge( b_k \vee c_k))$.
4. Each mutant $m$ is assessed by calling $\text{SAMPLE}(\textit{model,prior=m})$;
i.e. by seeing what can be achieved within a goal after first assuming
that $\textit{prior}=m$.
5. To test if the mutant $m$ is preferred to {\em pop}$_i$, OPTIMIZE uses
Zitler's continuous domination {\em cdom}
predicate~\cite{Zitzler2004}. This predicate compares two sets of objectives
from sets $x$ and $y$. In that comparison,
$x$ is better than another $y$ if $x$ ``losses'' least.
In the following, $``n''$ is the number of objectives and $w_j \in \{-1, 1\}$
shows if we seek to maximize
[
\begin{array}{rcl}
x \succ y & =& \textit{loss}(y,x) > \textit{loss}(x,y)\
\textit{loss}(x,y)& = &\sum_j^n -e^{\Delta(j,x,y,n)}/n\
\Delta(j,x,y,n) & = & w_j(o_{j,x} - o_{j,y})/n
\end{array}
]
OPTIMIZE repeatedly loops over the population, trying to replace items with mutants,
until new better mutants stop being found.
Return the population.
\\hline
\end{tabular}
\caption{Procedure OPTIMIZE: strives to find ``good'' priors which,
when passes to SAMPLE, maximize the number of edges used
while also minimizing cost, and
maximizing satisfied hard goals and soft goals.
OPTIMIZE is based on Storn's differential evolution optimizer~\protect\cite{storn1997differential}.
OPTIMIZE is called by the RANK procedure of \fig{rank}.
For the reader unfamiliar with the mutation technique of step 3 and the {\em cdom}
scoring of step5, we note that these\cite{Fu2016,krall2015gale}.
are standard practice in the search-based
SE community
}\label{fig:optimize}
Hence,
our learning objective can be generally described
as ``obtaining a classifier that will provide high accuracy for the minority class without severely compromising the accuracy of the majority class''.
. And found out that techniques like AdaBoost.NC had a better performance than the rest while others are planning to use SMOTE~\cite{gray20|
?? run tis into the last sentence "and they found that.."
it leaves open issue like
more generally , lit reviews must respect and disrespect. respectfully present others work, then point out their fatal mistake and why this work in needeed
t is important to select how many synthetic examples to create (
AUC(pf,pdf)
AUC(low, pd)
increase width of fig 2,3,4,5. make full page wide (but dont increase font size)
SMOTE's super-sampling selects instances from the minority class and finds k'' nearest neighbors for each instance and then creates new instances using the selected instances and their neighbors until we have
m'' numbers of minority class samples.
i think Ghotra et al [17] used AUC effort vs recall not AUC(pd,pf). please check
Results by Tantithamthavorn et al [50] also suggested that
every dataset comes with different attributes. And also clas-
sification techniques often have configurable parameters that
control characteristics of these classifiers that they produce.
Now time has come to even think about hyperparameter opti-
mization of these techniques and come up with an automated
process [2], [16] to tune these parameters for every dataset.
t then creates new instances using the selected instances and their neighbors
how?
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