Weka Tutorial 26: Semi-supervised Learning (Learning Techniques) Video Lecture | Weka Tutorial - Data & Analytics

hello YouTube welcome to another wake a
tutorial this tutorial is very important
because in this tutorial I'm going to
tell you how you can use semi-supervised
learning algorithms with wake up as you
know already that wake are developed by
University of Waikato doesn't have the
implementation of semi-supervised
learning algorithms it only has the
implementation of supervised and
clustering algorithms now if you were a
machine learning person then you might
have known that
nowaday semi-supervised learning
algorithms are popular choice because on
many occasions you can have much less
number of labeled data so whenever your
data set is not labeled that measure
it's very expensive to label data sets
in those cases semi-supervised learning
can be a viable option so wicked doesn't
have it then how are we going to
implement this in wake up well we don't
have to do it because as well cat is an
open source software there are many
people currently working on developing
semi-supervised learning algorithms in
Java and then they integrate that with
the wake of releases so these are the
files that I found after the extraction
of those five rar or zip files so in
this file you can find one folder called
target double click on that and you can
see a jar file executable Java archive
file called collective - classification
all you need to do is double click on
that so this one is actually opening or
running or executing the waker 3.7
version but integrated with
semi-supervised learning algorithm so
this is what a 2.7 + semi-supervised
learning algorithms so how do they look
like if we go to the Explorer and I'm
just randomly opening
one of my files from my disk actually
did this doesn't matter I'm just giving
you some demo so I'm choosing one of the
our files that I've produced in my
research I can go to I can click on the
classified tab go to the classifier
options and then you can see that we
have a new folder called collective so
this folder actually contains all the
semi-supervised learning algorithms
implemented by that group in Java and it
has been integrated with Weka 3.7 so if
you double click on that to open so you
can see there are soft folders that
means that we are having semi-supervised
learning outlining algorithms that can
be categorized into these four groups
functions lazy maida and trees so if I
close these three things you can see
that there are functions lazy maida and
tree here as well but these are the
supervised or clustering algorithms
produced by the University Waker is our
University of Waikato version of wake up
so if you go to the collective section
if you choose for example function ll GC
is a kind of semi supervised learning
algorithms and semi-supervised learning
algorithms to be honest are not as
popular as supervisor classification
sorry clustering algorithms so if you
don't know what this algorithm does or
how it works or what kind of data it
handles you can always select this one
ll GC or any other semi-supervised
learning algorithms under the collective
folder then you can left-click on these
options and you see that we have the
more button and you can click on the
more button to see how what it what it
is capable of doing and if you want to
know more about some of the some of the
implementation details of the algorithms
then you can always go back
to the references provided by the
author's here for example in order to
know ll GC you can always go back to
this 2003 paper that actually talks
about ll GC so this is kind of fun you
can play it play with it and you can
find out the power of algorithms
categorized under semi-supervised