In this technical report we presented a novel approach to machine learning. Once the new framework is presented, we will provide a simple and yet very powerful learning algorithm which will be benchmark on various dataset. The framework we proposed is based on booleen circuits; more specifically the classifier produced by our algorithm have that form. Using bits and boolean gates instead of rea...

The majority voting and accurate prediction of classification algorithm in data mining are challenging task for data classification. For the improvement of data classification used different classifier along with another classifier in a manner of ensemble process. Ensemble process increase the classification ratio of classification algorithm, now such par diagram of classification algorithm is ...

In this paper we propose a new method to improve the performance of hierarchical classification. We use a swarm intelligence algorithm to select the type of classification algorithm to be used at each “classifier node” in a classifier tree. These classifier nodes are used in a top-down divide and conquer fashion to classify the examples from hierarchical data sets. In this paper we propose a sw...

We have developed a Radar Echo Classifier (REC) algorithm to identify various radar echoes such as precipitation, clear air, and in particular anomalously propagated (AP) ground clutter in NEXRAD radar data. The REC uses fuzzy logic to identify these various echo types. We implemented the REC using the Python language along with C++ extensions. This implementation replaces and extends the funct...

Although classifier systems have displayed performance levels equaling or exceeding those of other techniques on a variety of benchmark classification problems, they usually solve those problems with a very large number of classifiers. In most cases, a large portion of the final classifier set is unneeded or wrong, with behavior masked by the correctly-functioning rules in the system. Wilson de...

The Linear discriminant analysis (LDA) can be generalized into a nonlinear form ─ kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experime...

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The proposed algorithm generates the probability set for each round using naïve Bayesian classifier and updates the weights of ...

Document classification aims to assign a document to one or more categories based on its contents. This paper suggests the use of Field association (FA) words algorithm with Naïve Bayes Classifier to the problem of document categorization of Arabic language Our experimental study shows that using FA algorithm with Naïve Bayes (NB) Classifier gives the ~ 79% average accuracy and, using compound ...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

This paper presents a method for the syntactic parsing of Hungarian natural language texts using a machine learning approach. This method learns tree patterns with various phrase types described by regular expressions from an annotated corpus. The PGS algorithm, an improved version of the RGLearn method, is developed and applied as a classifier in classifier combination schemas. Experiments sho...