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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

jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets

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jModelTest 2: more models, new heuristics and parallel computing.

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Data Mining Practical Machine Learning Tools and Techniques

ALTER is an open web-based tool to transform between different multiple sequence alignment formats The originality of ALTER lies in the fact that it focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats In addition, ALTER is capable of identify and remove identical sequences during the transformation process Besides its user-friendly environment, ALTER allows access to its functionalities in a programmatic way through a Representational State Transfer web service ALTER’s front-end and its API are freely available at http://singeiuvigoes/ALTER/ and http://singeiuvigoes/ALTER/api/, respectively

ALTER: program-oriented conversion of DNA and protein alignments

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

Applying lazy learning algorithms to tackle concept drift in spam filtering

Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.

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Web scraping technologies in an API world

Wireless Body Area Networks (WBANs) supporting healthcare applications are in early development stage but offer valuable contributions at monitoring, diagnostic, or therapeutic levels. They cover real-time medical information gathering obtained from different sensors with secure data communication and low power consumption. As a consequence of the increasing interest in the application of this type of networks, several articles dealing with different aspects of such systems have been published recently. In this paper, we compile and compare technologies and protocols published in the most recent researches, seeking WBAN issues for medical monitoring purposes to select the most useful solutions for this area of networking. The most important features under consideration in our analysis include wireless communication protocols, frequency bands, data bandwidth, transmission distance, encryption, authentication methods, power consumption, and mobility. Our study demonstrates that some characteristics of surveyed protocols are very useful to medical appliances and patients in a WBAN domain.

Wireless Body Area Networks for healthcare applications: protocol stack review

In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the target case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules.

Florentino Fdez-Riverola

Papers

ALTER: program-oriented conversion of DNA and protein alignments

Applying lazy learning algorithms to tackle concept drift in spam filtering

Web scraping technologies in an API world

Wireless Body Area Networks for healthcare applications: protocol stack review

SpamHunting: An instance-based reasoning system for spam labelling and filtering