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 Practical Machine Learning Tools and Techniques

Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.

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A Review of Feature Selection and Its Methods

Review: Personalizing recommendations for tourists

A deep learning method with wrapper based feature extraction for wireless intrusion detection system

Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

The success rate of computer science and engineering students in private universities are not high. It is helpful to find the model to assist students in registration planning. The objective of this research is to propose the classifier algorithm for building course registration planning model (CRPM) from historical dataset. The algorithm is selected by comparing performances of four classifiers include Bayesian network, C4.5, Decision Forest and NBTree. The dataset were obtained from student enrollments including grade point average (GPA) and grades of undergraduate students whose majors were computer science or computer engineering. These dataset included grades in each subject of first and second year students from a private university in Thailand. Results showed that NBTree seemed to be the best of four classifiers which had highest prediction power. NBTree was used to generate CRP model which can be used to predict student class of GPA and consider student course sequences for registration planning.

Comparisons of classifier algorithms: Bayesian network, C4.5, decision forest and NBTree for Course Registration Planning model of undergraduate students

In Thailand e-banking has been offered by various financial institutes including Thai commercial banks and government banks. However, e-banking in Thailand is not widely used and accepted as in other countries. Accordingly, the study of e-banking is scantly due to the limitation of data confidentiality. This study uses data mining techniques to analyse historical data of e-banking usages from a commercial bank in Thailand. These techniques including SOMS, K-Mean algorithm and marketing techniques-RFM analysis are used to segment customers into groups according to their personal profiles and e-banking usages. Then Apriori algorithm is applied to detect the relationships within features of e-banking services. Typically, results of this study are presented and can be used to generate new service packages which are customised to each segment of e-banking users.

/pdf/clustering-e-banking-customer-using-data-mining-and-4lcp0w74bh.pdf

Clustering e-Banking Customer using Data Mining and Marketing Segmentation

Small and medium enterprises (SMEs) in Thailand are fundamental business units spread all over the country. Since the severe economic crisis (i.e., Tom Yum Kung disease) in 1997, thousands of SMEs have gone bankrupt and so dropped out of the Thai economy each year. One key means of enhancing the viability of SMEs and assisting in economic recovery of the country that has been suggested is to transform them from a traditional to digital business using the Internet and e-commerce. The expected advantages of e-commerce strategy include decreasing costs, expanding marketplaces, enhancing competitiveness, improving business image, and increasing revenues.However, there are snares and hidden pitfalls in the backend of this business. This chapter presents an overview of e-commerce of SMEs in Thailand. The first part introduces fundamental background of SMEs in Thailand including types and characteristics. The second part investigates advantages and disadvantages of e-commerce implementation. Finally, the third part discusses SMEs and e-commerce in Thailand in the case of e-tourism.

The E-commerce of SMEs in Thailand

Recently information technology (IT) plays a significant role in business environment, enterprises use IT in the competitive world market. Web personalization and one to one marketing have been introduced as strategy and marketing tools. By using historical and present information of customers, organizations can learn, predict customer's behaviors and develop products or services best suited to potential customers.In this study, a Personalized Support System is suggested to manage traveling information for user. It provides the information that matches the users' interests. This system applies the Q Learning algorithm to analyze, learn customer behaviors and then it recommend products to meet customer interests. There are two learning approaches using in this study. First, Personalization Learner by Cluster Properties is learning from all users in one cluster to find the cluster interests of travel information by using given data on user ages and genders. Second, Personalization Learner by User Behavior: user profile, user behaviors and trip features will be analyzed to find the unique interest of each web user. The results from this study reveal that it is possible to develop Personalised Support System. Using weighted trip features improve effectiveness and increase the accuracy of the personalized engine. Precision, Recall and Harmonic Mean of the learned system are higher than the original one. This study offers new and fruitful information in the areas of web personalisation in tourist industry as well as in e-Commerce.

Anongnart Srivihok

Papers

Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

Comparisons of classifier algorithms: Bayesian network, C4.5, decision forest and NBTree for Course Registration Planning model of undergraduate students

Clustering e-Banking Customer using Data Mining and Marketing Segmentation

The E-commerce of SMEs in Thailand

E-commerce intelligent agent: personalization travel support agent using Q Learning