A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.

The paper presents a hybrid adaptation method that combines a knowledge-based approach with reinforcement learning and a simulated annealing technique, and is applied in the navigation of an educational mobile robot. The experimental results of simulations showed a good behaviour of the robot when doing an adaptive navigation in a dynamic environment by using the proposed hybrid method.

The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Näıve Bayes and Maximum Entropy. Using the well known...

In this paper we present an efficient hybrid classification algorithm based on combining case-based reasoning and random decision trees, which is based on a general approach for combining lazy and eager learning methods. We use this hybrid classification algorithm to predict the pain classification for palliative care patients, and compare the resulting classification accuracy to other similar ...

We present a hybrid machine learning approach for information extraction from unstructured documents by integrating a learned classifier based on the Maximum Entropy Modeling (MEM), and a classifier based on our work on Data–Oriented Parsing (DOP). The hybrid behavior is achieved through a voting mechanism applied by an iterative tag–insertion algorithm. We have tested the method on a corpus of...

A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...

In this paper, we consider the notion of distance/similarity metric learning method for classification and similarity search for face recognition purposes. We use the theory of filter banks, and Gabor wavelets for extraction of face features in three datasets: the ORL dataset, Yale face dataset, and ATT face dataset, and we compare the recognition performance with Gabor features to discrete Fou...

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, due to the fundamental differences in the design of the reactive and deliberative layers, the design of hybrid control architectures can pose significant difficulties. We propose a novel approach to improving system-level performance of hybrid contr...