6,144 research outputs found
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
Genetic Programming for Multibiometrics
Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined together (multiple captures of the same biometric modality, multiple
feature extraction algorithms, multiple biometric modalities...). In this
paper, we are interested in score level fusion functions application (i.e., we
use a multibiometric authentication scheme which accept or deny the claimant
for using an application). In the state of the art, the weighted sum of scores
(which is a linear classifier) and the use of an SVM (which is a non linear
classifier) provided by different biometric systems provide one of the best
performances. We present a new method based on the use of genetic programming
giving similar or better performances (depending on the complexity of the
database). We derive a score fusion function by assembling some classical
primitives functions (+, *, -, ...). We have validated the proposed method on
three significant biometric benchmark datasets from the state of the art
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System
Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier
Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction
Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian
classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the
frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio
(AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the
reduction of one of the most causes of pollution to produce greener environment
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
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