The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have ...

Supplementary Methods 1 2 Customizing the training sets 3 The format for the training files are described in the original RDP classifier 4 version 2.5 sample data folder that comes with the distribution available from 5 http://sourceforge.net/projects/rdp-classifier/ (Wang et al. 2007). For each of our training 6 sets, the two files we used to train the classifier are provided so that they can ...

Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maximization (RIM). RIM optimizes an intuitive information-theoretic objective function which balances class separation, class balance and classifier comple...

Predictive data mining is the process of automatically creating a classification model from a set of examples, called the training set, which belongs to a set of classes. Once a model is created, it can be used to automatically predict the class of other unclassified examples. Some datasets encountered in real life applications have skewed class distributions. Many predictive modeling systems a...

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many-class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications ...

A classification technique (or classifier) is a systematic approach used in building classification models from an input data set. Some examples include decision tree classifier, rule based classifiers, neural networks, support vector machines and naïve Bayes classifiers. Each technique employs a learning algorithm to identify a model that best fits the relationships between the attribute set a...

Recent works on ensemble methods like Adaptive Boosting have been applied successfully in many problems. Ada-Boost algorithm running on a given weak learner several times on slightly altered data and combining the hypotheses in order to achieve higher accuracy than the weak learner. This paper presents an expert system that boosts the performance of an ensemble of classifiers. In, Boosting, a s...

In this paper we aim to investigate the trade off in selection of an accurate, robust and costeffective classification model for binary classification problem. With empirical observation we present the evaluation of one-class and two-class classification model. We have experimented with four two-class and one-class classifier models on five UCI datasets. We have evaluated the classification mod...

A novel classifier, the so-called "LogitBoost" classifier, was introduced to predict the structural class of a protein domain according to its amino acid sequence. LogitBoost is featured by introducing a log-likelihood loss function to reduce the sensitivity to noise and outliers, as well as by performing classification via combining many weak classifiers together to build up a very strong and ...

A new boundary hunting radial basis function (BH-RBF) classifier which allocates RBF centers constructively near class boundaries is described. This classifier creates complex decision boundaries only in regions where confusions occur and corresponding RBF outputs are similar. A predicted square error measure is used to determine how many centers to add and to determine when to stop adding cent...