In this paper we introduced Tanimoto based similarity measure for host-based intrusions using binary feature set for training and classification. The k-nearest neighbor (kNN) classifier has been utilized to classify a given process as either normal or attack. The experimentation is conducted on DARPA-1998 database for intrusion detection and compared with other existing techniques. The introduc...

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN classifier may decrease the precision of classification because of the uneven density of t raining samples .In view of the defect, an improved k-nearest neighbor algorithm is presented using shared nearest neighbor similarity which can compute similarity between test ...

Similarity is a key concept for estimating associations among a set of objects. Music similarity is usually exploited to retrieve relevant items from a dataset containing audio tracks. In this work, we approach the problem of semantic similarity between short pieces of music by analysing their instrumentations. Our aim is to label audio excerpts with the most salient instruments (e.g. piano, hu...

The goal of this paper is to investigate the connection between the performance gain that can be obtained by selftraining and the similarity between the corpora used in this approach. Self-training is a semi-supervised technique designed to increase the performance of machine learning algorithms by automatically classifying instances of a task and adding these as additional training material to...

Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. Fusion is often based on fixed combination rules like the product and average. Only under strict probabilistic conditions can these rules be justified. We present here a simple rule for adapting the class combiner to the application. c decision templates (one per class) are estimated w...

The proposed method develops a fuzzy rule-based classifier that was tested using features for islanding detection in distributed generation. In the developed technique, the initial classification boundaries are found out by using the decision tree (DT). From the DT classification boundaries, the fuzzy membership functions (MFs) are developed and the corresponding rule base is formulated for isl...

With this submission, a set of ensemble learning based methods for the MIREX 2015 Speech / Music Classification and Detection task is proposed and evaluated. The main algorithm for the Detection task employs a self similarity matrix analysis technique to detect homogeneous segments of audio that can be subsequently classified as music or speech by a Random Forest classifier. In addition to the ...

Palomar Transient Factory is a comprehensive detection system for the identification and classification of transient astrophysical objects. The central piece in the identification pipeline is represented by an automated classifier that distinguishes between real and bogus objects with high accuracy. The classifier consists of two components—real-time and offline. Response time is the critical c...

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

classifier is highly dependant on the distance (or similarity) function used to find the NN of an input test pattern. In order to optimize the accuracy of the NN rule, a weighted similarity function is proposed. In this scheme, a weight is assigned to each training instance. The weights of training instances are used in the generalization phase to find the NN of an input test pattern. To specif...