Information retrieval systems depend on Boolean queries. Proposed evolution of Boolean queries should increase the performance of the information retrieval system. Information retrieval systems quality are measured in terms of two different criteria, precision and recall. Evolutionary techniques are widely applied for optimization tasks in different areas including the area of information retrieval systems. In information retrieval applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme 'harmonic mean'. Usage of genetic algorithms in the Information retrieval, especially in optimizing a Boolean query, is presented in this paper. Influence of both criteria, precision and recall, on quality improvement are discussed as well.
In this article we present a novel method for mobile phone positioning using a vector space model, suffix trees and an information retrieval approach. The algorithm is based on a database of previous measurements which are used as an index which looks for the nearest neighbor toward the query measurement. The accuracy of the algorithm is, in most cases, good enough to accomplish the E9-1-1 standards requirements on tested data. In addition, we are trying to look at the clusters of patterns that we have created from measured data and we have reflected them to the map. We use Self-Organizing Maps for these purposes.
Linear ordering problem is a well-known optimization problem attractive for its complexity (it is an NP-hard problem), rich library of test data and variety of real world applications. In this paper, we investigate the use and performance of two variants of genetic algorithms, mutation only genetic algorithms and higher level chromosome genetic algorithm, on the linear ordering problem. Both methods are tested and evaluated on a library of real world and artificial linear ordering problem instances.
An information retrieval (IR) system (IRs) (search engine) is said to be efficient, to the degree that always evaluates each object in the information base (database, document base, web,...) like the expert. The ability of IRs's is to retrieve mostly all relevant objects (measured by the recall), and only the (most) relevant objects (measured by the precision) from the collection queried.
Recall and precision measures provide the classical measure of the retrieval efficiency. They measure the degree to which the query answer (the set of documents that retrieved by IRs as response to the user query). Where, the query answer is the set of relevant documents in the information based queried.
Retrieving most relevant documents to the user query in IRs was one of the most important methods of World Wide Web (WWW) search engines used in the world now. So the searchers aim to use genetic programming (GP) and fuzzy optimization to optimize the user search query in the Boolean IRs model and in the fuzzy IRs model; and to use more Boolean operators (AND, OR, XOR, OF, and NOT) instead of using the standard operators (AND, OR, and NOT), and to use weights for terms and for Boolean operators. Weights are used to give the users more relaxation in defining how much the importance of the terms and of the Boolean operators is. The terms and the Boolean operators' weights are used in fuzzy IRs model. In addition, it investigates extensions of the classical measurement of effectiveness in IRs, precision; recall and harmonic mean.
The researchers use harmonic mean measure as an objective function which uses both measures precision and recall at once for evaluating the results of the two IRs models to grow up the precision-recall relationship curve.
Since their appearance in 1993, first approaching the Shannon limit, turbo codes have given a new direction in the channel encoding field, especially since they have been adopted for multiple norms of telecommunications such as deeper communication. A robust interleaver can significantly contribute to the overall performance a turbo code system. Search for a good interleaver is a complex combinatorial optimization problem. In this paper, we present genetic algorithms and differential evolution, two bio-inspired approaches that have proven the ability to solve non-trivial combinatorial optimization tasks, as promising optimization methods to find a well-performing interleaver for large frame sizes.
Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background and initial version of Genetic Algorithm for binary matrix factorization.
In this article we use a combination of neural networks with other techniques for the analysis of orthophotos. Our goal is to obtain results that can serve as a useful groundwork for interactive exploration of the terrain in detail. In our approach we split an aerial photo into a regular grid of segments and for each segment we detect a set of features. These features depict the segment from the viewpoint of a general image analysis (color, tint, etc.) as well as from the viewpoint of the shapes in the segment. We perform clustering based on the Formal Concept Analysis (FCA) and Non-negative Matrix Factorization (NMF) methods and project the results using effective visualization techniques back to the aerial photo. The FCA as a tool allows users to be involved in the exploration of particular clusters by navigation in the space of clusters. In this article we also present two of our own computer systems that support the process of the validation of extracted features using a neural network and also the process of navigation in clusters. Despite the fact that in our approach we use only general properties of images, the results of our experiments demonstrate the usefulness of our approach and the potential for further development.
Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.