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

Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis

State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for secondary usage applications SOH estimation based on machine learning has attracted great attention in recent years and holds potentials for battery informatization and cloud battery management techniques In this article, a comprehensive study of the data-driven SOH estimation methods is conducted A new classification for health indicators (HIs) is proposed where the HIs are divided into the measured variables and calculated variables To illustrate the significance of data preprocessing, four noise reduction methods are assessed in the HIs extraction process; different feature selection methods, including filter-based method, wrapper-based method, and fusion-based method, are applied to select HIs subsets The four widely used machine learning algorithms, including artificial neural network, support vector machine, relevance vector machine, and Gaussian process regression (GPR), are applied and compared In order to evaluate the estimation performance in potential real usages under future big data era, the three HIs selection methods and four machine learning methods are evaluated using three public data sets and two estimation strategies The results show that the combination of the fusion-based selection method and GPR has an overall superior estimation performance in terms of both accuracy and computational efficiency

Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Classification is a technique used for discovering classes of unknown data. Various methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant attributes needs to be filtered. Filtering is done using different feature selection techniques like wrapper, filter, embedded technique. This paper is an introductory paper on different techniques used for classification and feature selection.

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Sunita Beniwal

Papers

Classification and Feature Selection Techniques in Data Mining