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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

https://fada.birzeit.edu/bitstream/20.500.11889/5314/1/Hybrid%20Whale%20Optimization%20Algorithm%20with%20Simulated%20Annealing%20for%20FS.pdf

Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection

Although technology provides numerous benefits to young people, it also has a ’ dark side ’, as it can be used for harm, not only by some adults but also by the young people themselves. Email, texting, chat rooms, mobile phones, mobile phone cameras and web sites can and are being used by young people to bully peers. It is now a global problem with many incidents reported in the United States, Canada, Japan, Scandinavia and the United Kingdom, as well as in Australia and New Zealand. This growing problem has as yet not received the attention it deserves and remains virtually absent from the research literature. This article explores definitional issues, the incidence and potential consequences of cyber bullying, as well as discussing possible prevention and intervention strategies.

Cyber bullying: An old problem in a new guise?

We developed a hybrid method for feature selection of classification tasks.Our hybrid method combines Artificial Bee Colony with Differential Evolution.We performed experiments over fifteen datasets from the UCI Repository.Our method selects good features without reducing accuracy of classification.By selecting features with our method, we reduced time required for classification. "Dimensionality" is one of the major problems which affect the quality of learning process in most of the machine learning and data mining tasks. Having high dimensional datasets for training a classification model may lead to have "overfitting" of the learned model to the training data. Overfitting reduces generalization of the model, therefore causes poor classification accuracy for the new test instances. Another disadvantage of dimensionality of dataset is to have high CPU time requirement for learning and testing the model. Applying feature selection to the dataset before the learning process is essential to improve the performance of the classification task. In this study, a new hybrid method which combines artificial bee colony optimization technique with differential evolution algorithm is proposed for feature selection of classification tasks. The developed hybrid method is evaluated by using fifteen datasets from the UCI Repository which are commonly used in classification problems. To make a complete evaluation, the proposed hybrid feature selection method is compared with the artificial bee colony optimization, and differential evolution based feature selection methods, as well as with the three most popular feature selection techniques that are information gain, chi-square, and correlation feature selection. In addition to these, the performance of the proposed method is also compared with the studies in the literature which uses the same datasets. The experimental results of this study show that our developed hybrid method is able to select good features for classification tasks to improve run-time performance and accuracy of the classifier. The proposed hybrid method may also be applied to other search and optimization problems as its performance for feature selection is better than pure artificial bee colony optimization, and differential evolution.

A hybrid approach of differential evolution and artificial bee colony for feature selection

A comparative study on the effect of feature selection on classification accuracy

The incredible increase in the amount of information on the World Wide Web has caused the birth of topic specific crawling of the Web During a focused crawling process, an automatic Web page classification mechanism is needed to determine whether the page being considered is on the topic or not In this study, a genetic algorithm (GA) based automatic Web page classification system which uses both HTML tags and terms belong to each tag as classification features and learns optimal classifier from the positive and negative Web pages in the training dataset is developed Our system classifies Web pages by simply computing similarity between the learned classifier and the new Web pages In the existing GA-based classifiers, only HTML tags or terms are used as features, however in this study both of them are taken together and optimal weights for the features are learned by our GA It was found that, using both HTML tags and terms in each tag as separate features improves accuracy of classification, and the number of documents in the training dataset affects the accuracy such that if the number of negative documents is larger than the number of positive documents in the training dataset, the classification accuracy of our system increases up to 95% and becomes higher than the well known Naive Bayes and k nearest neighbor classifiers

A Web page classification system based on a genetic algorithm using tagged-terms as features

The increased use of the Internet and the ease of access to online communities like social media have provided an avenue for cybercrimes. Cyberbullying, which is a kind of cybercrime, is defined as an aggressive, intentional action against a defenseless person by using the Internet, social media, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. The aim of this study is to detect cyberbullying on social media messages written in Turkish. To our knowledge, this is the first study which makes cyberbully detection on Turkish texts. We prepare a dataset from Instagram and Twitter messages written in Turkish and then we applied machine learning techniques that are Support Vector Machines (SVM), decision tree (C4.5), Naive Bayes Multinomial, and k Nearest Neighbors (kNN) classifiers to detect cyberbullying. We also apply information gain and chi-square feature selection methods to improve the accuracy of classifiers. We observe that when both words and emoticons in the text messages are taken into account as features, cyberbully detection improves. Among the classifiers, Naive Bayes Multinomial is the most successful one in terms both classification accuracy and running time. When feature selection is applied classification accuracy improves up to 84% for the dataset used.

Detection of cyberbullying on social media messages in Turkish

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C45, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods

/pdf/an-ant-colony-optimization-based-feature-selection-for-web-228qts40er.pdf

Selma Ayşe Özel

Papers

A hybrid approach of differential evolution and artificial bee colony for feature selection

A comparative study on the effect of feature selection on classification accuracy

A Web page classification system based on a genetic algorithm using tagged-terms as features

Detection of cyberbullying on social media messages in Turkish

An ant colony optimization based feature selection for web page classification.