Category: Machine Learning

Feature extraction from retina vascular images for classification

Classifying medical images is tedious and complex task. Using machine learning algorithms to assist the process could be a huge help. There are many challenges to make machine learning algorithms work reliably on image data. First of all you need rather large image database with ground truth information (expert’s labeled data with diagnosis information). Second problem is preprocessing images, including merging modalities, unifying color maps, normalizing and filtering. This part is important and may impact last part – feature extraction. This step is crucial, because on how well you are able to extract informative features, depends how well machine learning algorithms will work. Dataset In order to demonstrate classification procedure of medical images the ophthalmology STARE (STructured Analysis of the Retina) image database was pulled from http://cecas.clemson.edu/~ahoover/stare/. The database consist of 400 images with 13 diagnostic cases along with preprocessed images. For classification problem we have chosen only vessels images…

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Running remote host Weka experiments

Previously we have tried to run weka server to utilize all cores of processor in classification tasks. But appears that wekaserver works only in explorer for classification routines. For more advanced machine learning there is more flexible tool – experimenter. Weka server doesn’s support this area. So what to do if you want more performance or simply utilize multi-core processor of local machine. There is a way out, but it is more tricky. Weka has ability to perform remote experiments that allow spreading the load across multiple host machines that have Weka set up. You can read the documentation of remote experiment on Weka wikispaces, but in some cases it may be somewhat confusing. It took time for me to figure out some parts by trial and error. The trickiest part is to set everything up and prepare the necessary command to be run before performing remote experiment. So lets…

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Utilizing multi-core processor for classification in WEKA

Currently WEKA is one of the most favorites machine learning tools. Without programming skills you can do serious classification, clustering and big data analysis. For some time I’ve been using its standard GUI features without thinking much about performance bottlenecks. But since researches are becoming more complex by using ensemble, voting and other meta-algorithms that normally are based on multiple classifiers running simultaneously, the performance issues start becoming annoying. You need to wait for hours until task is completed. The problem is that when running classification algorithms from the WEKA GUI, the utilize a single core of your processor. Such algorithms as Multi-layer Percepron running 10 fold cross-validation is calculating one cross fold at the time on one core taking long time to accomplish: So I started looking for options to make it use all cores of processor as separate threads for each fold of operation. There are couple options…

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Implementing logistic regression learner with python

Logistic regression is a next step from linear regression. The most real life data have non linear relationship, thus applying linear models might be ineffective. Logistic regression is capable of handling hon linear effects in prediction tasks. You can think of lots of different scenarios where logistic regression could be applied. There can be financial, demographic, health, weather and other data where model could be applied and used to predict next events on upcoming data. For instance you can classify emails in to span and non spam, transactions being fraud or non, tumors being malignant or benign. In order to understand logistic regression, let’s cover some basics, do a simple classification on data set with two features and then test it on real life data with multiple features.

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Future of Artificial Intelligence; Its Implementations and Limits

Artificial Intelligence (AI) is the field of computer science which uses mechanical and computational processes to echo almost all aspects of human intelligence. AI can perform multiple functions: sensory interaction with the environment and the ability to make decisions about events that aren’t happened yet without any human assistance whatsoever. Targeted advertising and virtual agents that recognize the patterns of your behavior are much standard in today’s online undertakings. Artificial Intelligence is used by business enterprises in data analysis algorithms which have the highest advantage of analyzing the Big Data, and it also involves customer engaging techniques. Apart from IBM which developed some of the earliest functions of AI, Google and Facebook are also using AI for the analytic purpose of the massive amount of data they receive.

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Building and evaluating Naive Bayes classifier with WEKA

This is a followup post from previous where we were calculating Naive Bayes prediction on given data set. This time I want to demonstrate how all this can be implemented using WEKA application. For those who doesn’t know what WEKA is I highly recommend visiting their website and getting latest release. It is really powerful machine learning software written in Java. You can find plenty of tutorials in youtube on how to get started with WEKA. So I wont get in to details. I’m sure you’ll be able to follow anyway.

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Simple explanation of Naive Bayes classifier

Probably you’ve heard about Naive Bayes classifier and likely used in some GUI based classifiers like WEKA package. This is a number one algorithm used to see the initial results of classification. Sometimes surprisingly it outperforms the other models with speed, accuracy and simplicity. Lets see how this algorithm looks and what does it do. As you may know algorithm works on Bayes theorem of probability which allows to predict the class of unknown data set. Hoe you are comfortable with probability math – at least some basics.

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