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.

A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems

https://cronfa.swan.ac.uk/Record/cronfa49214/Download/0049214-17032019223053.pdf

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Internet of Things and Big Data as enablers for business digitalization strategies

Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm

Nature-inspired algorithms have gained immense popularity in recent years to tackle hard real world (NP hard and NP complete) problems and solve complex optimization functions whose actual solution doesn’t exist. The paper presents a comprehensive review of 12 nature inspired algorithms. This study provides the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of required input parameters, their key evolutionary strategies and application areas. A list of automated toolboxes available for directly evaluating these nature inspired algorithms over numerical optimization problems indicates the need for unified toolbox for all nature inspired algorithms. It also elucidates the users with the minimum and maximum dimensions over which these algorithms have already been evaluated on benchmark test functions. Hence this study would aid the research community to know what all algorithms could be examined for large scale global optimization to overcome the problem of ‘curse of dimensionality’.

/pdf/nature-inspired-algorithms-state-of-art-problems-and-ry92be0fz5.pdf

Nature-Inspired Algorithms: State-of-Art, Problems and Prospects

Concept drift in Streaming Data Classification: Algorithms, Platforms and Issues

In this research work, we have proposed a novel ensemble learning approach “BBS method” which stands for Bagging, Boosting and Stacking with appropriate base classifiers for the classification of the five UCI datasets taken from the field of Bioinformatics. Experiments are conducted using Weka and Java Eclipse and it has been observed empirically that our approach gives better accuracy with lower root mean square error rate using the technique of ensemble learning. Henceforth we conclude that our proposed ensemble learning method is more suitable in handling the classification problem in the bioinformatics domain. Such approaches can be efficiently used in related real-life scenarios of classification domain.

A comparative study of ensemble learning methods for classification in bioinformatics

Web based Decision Support systems like recommendation systems have become effective tools for decision making in the recent past. However the recommender systems employing conventional clustering techniques (KRS) like K-Means for collaborative filtering, suffer from the limitation of getting local optimum results. This paper presents Memetic Recommender System (MRS) based on the collaborative behavior of memes. Memetic Algorithms (MAs) are considered as one of the most successful approaches for combinatorial optimization. MAs are the genetic algorithms which incorporate local search in the evolutionary scheme. We propose a distinctive strategy to perform local search in memetic algorithms. MRS works in 2 phases-In the first phase a model is developed based on Memetic Clustering algorithm and in the second phase trained model is used to predict recommendations for the active user. Rigorous experiments were conducted to prove the decision support and statistical efficacy of MRS visa vis KRS. Results confirmed that the proposed approach yields much better performance as compared to the conventional collaborative filtering recommender system.

Shikha Mehta

Papers

Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm

Nature-Inspired Algorithms: State-of-Art, Problems and Prospects

Concept drift in Streaming Data Classification: Algorithms, Platforms and Issues

A comparative study of ensemble learning methods for classification in bioinformatics

Memetic Collaborative Filtering Based Recommender System