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

Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

/pdf/a-systematic-review-of-healthcare-applications-for-2xnx964c6m.pdf

A Systematic Review of Healthcare Applications for Smartphones

In microblogging services such as Twitter, the users may become overwhelmed by the raw data One solution to this problem is the classification of short text messages As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages

/pdf/short-text-classification-in-twitter-to-improve-information-3s8z7bfqxf.pdf

Short text classification in twitter to improve information filtering

Naive Bayes is very popular in commercial and open-source anti-spam e-mail filters. There are, however, several forms of Naive Bayes, something the anti-spam literature does not always acknowledge. We discuss five dierent versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages. The new datasets, which we make publicly available, are more realistic than previous comparable benchmarks, because they maintain the tempo- ral order of the messages in the two categories, and they emulate the varying proportion of spam and ham messages that users receive over time. We adopt an experimental procedure that emulates the incremental training of person- alized spam filters, and we plot roc curves that allow us to compare the dierent versions of nb over the entire tradeo between true positives and true negatives.

http://nlp.cs.aueb.gr/pubs/ceas2006_paper.pdf

Spam Filtering with Naive Bayes - Which Naive Bayes?

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

/pdf/overview-of-biocreative-ii-gene-mention-recognition-30lskb0log.pdf

Overview of BioCreative II gene mention recognition

In the recent years, we have witnessed a dramatic increment in the volume of spam email. Other related forms of spam are increasingly revealing as a problem of importance, specially the spam on Instant Messaging services (the so called SPIM), and Short Message Service (SMS) or mobile spam.Like email spam, the SMS spam problem can be approached with legal, economic or technical measures. Among the wide range of technical measures, Bayesian filters are playing a key role in stopping email spam. In this paper, we analyze to what extent Bayesian filtering techniques used to block email spam, can be applied to the problem of detecting and stopping mobile spam. In particular, we have built two SMS spam test collections of significant size, in English and Spanish. We have tested on them a number of messages representation techniques and Machine Learning algorithms, in terms of effectiveness. Our results demonstrate that Bayesian filtering techniques can be effectively transferred from email to SMS spam.

Content based SMS spam filtering

We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a low-bandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bag-of-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.

/pdf/spam-filtering-for-short-messages-5bc9xlafbc.pdf

Spam filtering for short messages

Mobile spam in an increasing threat that may be addressed using filtering systems like those employed against email spam. We believe that email filtering techniques require some adaptation to reach good levels of performance on SMS spam, especially regarding message representation. In order to test this assumption, we have performed experiments on SMS filtering using top performing email spam filters on mobile spam messages using a suitable feature representation, with results supporting our hypothesis.

Feature engineering for mobile (SMS) spam filtering

Spam filtering is a text categorization task that shows especial features that make it interesting and difficult. First, the task has been performed traditionally using heuristics from the domain. Second, a cost model is required to avoid misclassification of legitimate messages. We present a comparative evaluation of several machine learning algorithms applied to spam filtering, considering the text of the messages and a set of heuristics for the task. Cost-oriented biasing and evaluation is performed.

Combining text and heuristics for cost-sensitive spam filtering

In recent years, email spam has become an increasingly important problem, with a big economic impact in society. In this work, we present the problem of spam, how it affects us, and how we can fight against it. We discuss legal, economic, and technical measures used to stop these unsolicited emails. Among all the technical measures, those based on content analysis have been particularly effective in filtering spam, so we focus on them, explaining how they work in detail. In summary, we explain the structure and the process of different Machine Learning methods used for this task, and how we can make them to be cost sensitive through several methods like threshold optimization, instance weighting, or MetaCost. We also discuss how to evaluate spam filters using basic metrics, TREC metrics, and the receiver operating characteristic convex hull method, that best suits classification problems in which target conditions are not known, as it is the case. We also describe how actual filters are used in practice. We also present different methods used by spammers to attack spam filters and what we can expect to find in the coming years in the battle of spam filters against spammers.

Enrique Puertas Sanz

Papers

Content based SMS spam filtering

Spam filtering for short messages

Feature engineering for mobile (SMS) spam filtering

Combining text and heuristics for cost-sensitive spam filtering

Email spam filtering