Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users' needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms. �

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Applying genetic algorithms to information retrieval using vector space model

Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.

Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

Content based image retrieval (CBIR) systems work by retrieving images which are related to the query image (QI) from huge databases. The available CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. Where, features are extracted from the given QI in the similar fashion. Consequently, a novel similarity evaluation using a meta-heuristic algorithm called a memetic algorithm (genetic algorithm with great deluge) is achieved between the features of the QI and the features of the database images. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset) and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision.

An efficient similarity measure for content based image retrieval using memetic algorithm

The relevant documents from large data sets are retrieved with the help of ranking function in Information Retrieval system. In this paper, a new fuzzy logic based ranking function is proposed and implemented to enhance the performance of Information Retrieval system. The proposed ranking function is based on the computation of different terms of term-weighting schema such as term frequency, inverse document frequency and normalization. Fuzzy logic is used at two levels to compute relevance score of a document with respect to the query in present work. All the experiments are performed on CACM and CISI benchmark data sets. The experimental results reveal that the performance of our proposed ranking function is much better than the fuzzy based ranking function developed by Rubens along with other widely used ranking function Okapi-BM25 in terms of precision, recall and F-measure.

A new fuzzy logic based ranking function for efficient Information Retrieval system

This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function. Keywords—Cosine similarity, Fitness function, Genetic Algorithm, Information Retrieval, Query learning.

Osman A. Sadek

Papers

Using Genetic Algorithm to Improve Information Retrieval Systems