I'm planning to develop program in Java which will provide diagnosis. The data set is divided into two parts one for training and the other for testing. My program should learn to classify from the training data (BTW which contain answer for 30 questions each in new column, each record in new line the last column will be diagnosis 0 or 1, in the testing part of data diagnosis column will be empty - data set contain about 1000 records) and then make predictions in testing part of data :/
I've never done anything similar so I'll appreciate any advice or information about solution to similar problem.
I was thinking about Java Machine Learning Library or Java Data Mining Package but I'm not sure if it's right direction... ? and I'm still not sure how to tackle this challenge...
Please advise.
All the best!
I strongly recommend you use Weka for your task
Its a collection of machine learning algorithms with a user friendly front-end which facilitates a lot of different kinds of feature and model selection strategies
You can do a lot of really complicated stuff using this without really having to do any coding or math
The makers have also published a pretty good textbook that explains the practical aspects of data mining
Once you get the hang of it, you could use its API to integrate any of its classifiers into your own java programs
Hi As Gann Bierner said, this is a classification problem. The best classification algorithm for your needs I know of is, Ross Quinlan algorithm. It's conceptually very easy to understand.
For off-the-shelf implementations of the classification algorithms, the best bet is Weka. http://www.cs.waikato.ac.nz/ml/weka/. I have studied Weka but not used, as I discovered it a little too late.
I used a much simpler implementation called JadTi. It works pretty good for smaller data sets such as yours. I have used it quite a bit, so can confidently tell so. JadTi can be found at:
http://www.run.montefiore.ulg.ac.be/~francois/software/jaDTi/
Having said all that, your challenge will be building a usable interface over web. To do so, the dataset will be of limited use. The data set basically works on the premise that you have the training set already, and you feed the new test dataset in one step, and you get the answer(s) immediately.
But my application, probably yours also, was a step by step user discovery, with features to go back and forth on the decision tree nodes.
To build such an application, I created a PMML document from my training set, and built a Java Engine that traverses each node of the tree asking the user to give an input (text/radio/list) and use the values as inputs to the next possible node predicate.
The PMML standard can be found here: http://www.dmg.org/ Here you need the TreeModel only. NetBeans XML Plugin is a good schema-aware editor for PMML authoring. Altova XML can do a better job, but costs $$.
It is also possible to use an RDBMS to store your dataset and create the PMML automagically! I have not tried that.
Good luck with your project, please feel free to let me know if you need further inputs.
There are various algorithms that fall into the category of "machine learning", and which is right for your situation depends on the type of data you're dealing with.
If your data essentially consists of mappings of a set of questions to a set of diagnoses each of which can be yes/no, then I think methods that could potentially work include neural networks and methods for automatically building a decision tree based on the test data.
I'd have a look at some of the standard texts such as Russel & Norvig ("Artificial Intelligence: A Modern Approach") and other introductions to AI/machine learning and see if you can easily adapt the algorithms they mention to your particular data. See also O'Reilly, "Programming Collective Intelligence" for some sample Python code of one or two algorithms that might be adaptable to your case.
If you can read Spanish, the Mexican publishing house Alfaomega have also published various good AI-related introductions in recent years.
This is a classification problem, not really data mining. The general approach is to extract features from each data instance and let the classification algorithm learn a model from the features and the outcome (which for you is 0 or 1). Presumably each of your 30 questions would be its own feature.
There are many classification techniques you can use. Support vector machines is popular as is maximum entropy. I haven't used the Java Machine Learning library, but at a glance I don't see either of these. The OpenNLP project has a maximum entropy implementation. LibSVM has a support vector machine implementation. You'll almost certainly have to modify your data to something that the library can understand.
Good luck!
Update: I agree with the other commenter that Russel and Norvig is a great AI book which discusses some of this. Bishop's "Pattern Recognition and Machine Learning" discusses classification issues in depth if you're interested in the down and dirty details.
Your task is classical for neural networks, which are intended first of all to solve exactly classification tasks. Neural network has rather simple realization in any language, and it is the "mainstream" of "machine learning", closer to AI than anything other.
You just implement (or get existing implementation) standart neural network, for example multilayered network with learning by error back propagation, and give it learning examples in cycle. After some time of such learning you will get it working on real examples.
You can read more about neural networks starting from here:
http://en.wikipedia.org/wiki/Neural_network
http://en.wikipedia.org/wiki/Artificial_neural_network
Also you can get links to many ready implementations here:
http://en.wikipedia.org/wiki/Neural_network_software
Related
What algorithm does Named Entity Recognition (NER) use? I mean how does it match and tag all the entities?
Basically, There are lot of algorithm Named Entity Recognition but the most famous are
Conditional Random Field(CRF) Algorithm is used.
Stanford CoreNLP for Java
Spacy
NLTK
Another most effective one is using Deep Learning like Recurrent Neural Network (LSTM).
http://nlp.town/blog/ner-and-the-road-to-deep-learning/
https://towardsdatascience.com/deep-learning-for-ner-1-public-datasets-and-annotation-
methods-8b1ad5e98caf
there are lot other articles and paper out there
Hope this will help :)
NER can be performed by different algorithms, from simple string matching using grep: http://labs.fc.ul.pt/mer/ to advanced machine learning techniques: https://nlp.stanford.edu/software/CRF-NER.shtml
Basically, It Depends on the approach that you are following.
For Name Entity Recognition There are n no.of approaches available. Most typically Conditional Random Field(CRF) Algorithm is used. Another most effective one is using Deep Learning like Recurrent Neural Network (LSTM). There might be other algorithms as well.
Hope this will help :)
If you want to play around with a nice, pre-built Bidirectional CRF-LSTM (currently one of the better performing models) in Keras, I would recommend the python package Anango as a very quick way to play around with an NER system.
Although this initial question is tagged with Java. It is important to point out that you are going to find all the latest and greatest algorithms for this type of Machine Learning implemented in Python.
Spacy (a Python package) is also a very good way to get started although I found it took a little more time to set something up quickly. Also, I'm not sure how easy it would be to alter their NER algorithm that is pre-built. I believe it uses CNNs.
A good overview of where things are at with NER:
A Survey on Recent Advances in Named Entity Recognition from Deep
Learning models
I am using Java (but am open to solutions in other languages as well). I am looking at open source predictive modeling solutions for guessing what GUI/application features a user is interested in (I will have the specific user behavior data on the GUI/application). Instead of just looking at most used actions etc, should I possibly look at incorporating SVM or decision trees? I am looking at weka, mahout and jahmm - is there any other resource I can look at (specifically for GUI behavior - which hopefully returns results fast enough even if accuracy is reduced). Since I am not extremely knowledgeable about this field, please inquire about any information I may have left out to better ascertain a working solution. Thanks!
It's incredibly difficult to say given that we don't know what data you're using (I don't know of existing software to do this, but it may very well exist). With respect to support vector machines, they are binary or one-versus all classifiers, so I don't think they would be applicable here, if I understand your intentions correctly.
If you're unfamiliar with machine learning, Weka may be a good place for you to start. If you have supervised data, then you can feed all of your feature vectors with associated classification data into Weka and use cross-validation to see what type of technique suits you best. Additionally, you can use Weka to see if certain features are more important than others and do manual dimensionality reduction. Or of course, you can use one of Weka's dimensionality reduction techniques, but it may be difficult to decide which one if you don't know the assumptions that they make or how your data is related (this also applies to whatever prediction technique you try/use). Although, if you have enough time, you can just play around and manually just see what works best.
I have read the explanation in http://en.wikipedia.org/wiki/PageRank and i understand that the page rank is calculated by incoming links and out going links.
I have a crawler while crawls a webpage and store in db i need an page-rank algorithm.
I have a db with following values
Title
url
content_html
outgoing_links(external domain)
internal_links(the links with same domain of the url)
can u please explain do i need any other value to compute the page rank and. please explain how to compute it using java
PageRank is, at its heart, a linear algebra eigenvalue problem:
http://www.rose-hulman.edu/~bryan/googleFinalVersionFixed.pdf
If you don't know linear algebra or eigenvalue problems, or aren't willing to read this paper, it's unlikely that you'll be able to tackle this problem. As Einstein said, "Make the problem as simple as possible, but no simpler..."
The paper's title is old; it refers to Google's market cap circa 2004. It's up to $211B this morning.
The technology hasn't stood still in all that time. Google continues to tweak the algorithm in proprietary ways. But this paper explains the heart of it.
You have a few options. If you want to do it all yourself then duffymo's solution is perfect but if you want to use existing libraries I would suggest something similar to Jung for graphs.
I'm not sure if your familiar with graphs but they can be used to store the structure of the links and pagerank is often included in most libraries. Depending on how you want to do it, a good in memory solution is Jung but if you need persistent database storage than loading your data into Neo4J would work(you would have to install gremlin to do the pagerank).
The above are Java solutions but if you want to do it yourself(and like me don't like dry research papers) then I would highly suggest the book programming collective intelligence. They go through(chapter 4? I think) creating a search engine from scratch that includes pagerank and neural networks to monitor clicks. The only problem, based on your requirements above, is the book is written in python but you can easily apply the logic to java. If you know a bit of python already then you can even download the books source code for free and check out the software(but there is no explanation on the math behind the code in the source code).
Hope that helps
I'm planning on building a feature for an e-commerce platform I developed in Java to display related products in much the same way Amazon does. There are a few different metrics for relating products that I want to explore.
Purchase history (purchased at the same time)
Related by family/type (similar product classifications)
Intentionally related (boosting results; "Buy this!")
While I would probably be able to develop my own datamining library, it wouldn't be very portable and I dare say it wouldn't be very good either.
There are several packages out there for doing this sort of thing but I don't feel like I am in a position to evaluate which package or solution would work best for me. Any input anecdotal or from personal experience would be greatly appreciated.
Note: I've tagged this as Neural networking because of a python talk I was at where a neural-like-network was used for datamining, I'm not convinced a neural network is the best choice for this job.
Take a look at Apache Mahout
There are some artificial algorithm techniques used for data mining, such as C4.5 or ID3. These algorithm does classification. Other techniques such as ant clustering, neural networks or genetic algorithms are used for classification purposes in data mining.
As far as algorithms, I don't know much but ID3/C4.5 can be easily programmed.
Hope this helps.
I have heard of this algorithm, but is this algorithm good to use it with Bayesian Belief networks? Hugin is based on it and I'm looking for a book / article on this algorithm.
The algorithm is described in this paper. It is quite detailed and should be a good point to start.
I haven't kept track of this research area for a while, but I can point you towards the
CiteSeerX search engine if you don't know it already. (http://citeseerx.ist.psu.edu/)
Searching for papers which cite Shenoy & Shafer's An axiomatic framework for Bayesian and belief function propagation (1990) will give you a list of other researchers who have tried to apply the algorithm.
I am not familiar with the algorithm but another place to check for information would be
a search in google scholar.
Pulcinella is a tool for Propagating Uncertainty through Local Computations based on the general framework af valuation systems proposed by Shenoy and Shafer
Pulcinella is freely available for
educational and strictly
non-commercial use. Pulcinella is
written in Common Lisp. It has been
tested on Allegro CL on Macintosh, and
on Lucid CL, Allegro CL, and CLisp on
a Sun. The code is just "pure" common
lisp, so it should also run on any
other reasonable implementation of
common-lisp (well, you know...). To
get the latest version, click here.
Alternatively, you can get Pulcinella
by anonymous ftp from
ftp://aass.oru.se/pub/saffiotti. The
Pulcinella tar archive includes a few
examples, taken from the User's
Manual. If you fetch this program, you
are expected to send a letter at the
address below, stating that you will
use Pulcinella for research and
non-commercial use only.
Also here is some references.
Even More references:
An Algorithm for Bayesian Belief Network Construction from Data
A Tutorial on Learning With Bayesian Networks
http://en.wikipedia.org/wiki/Bayesian_network#External_links