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.

/pdf/a-review-of-feature-selection-and-its-methods-172ufuz4e8.pdf

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

Data classification is one technique of data mining using for creating knowledge map. The knowledge map describes who has what tacit knowledge and where the knowledge is collected. Knowledge map further helps employees to learn the jobs and expertise in organization. In many organizations there is a lack of guidelines for knowledge management and knowledge mapping. The objectives of this paper is to propose a classification algorithm for creating knowledge map of experts in an organization. The data set using in this paper are in the domain of energy experts. The performances of four classification algorithms are measured by comparing their prediction powers on expert classification. Four candidate algorithms include two types of Decision Trees (ID3 and C4.5), and two Rule-based (OneR and Prism). Results show that C4.5 algorithm is the best one, while ID3 is the last one in classification for knowledge mapping. Future research and implication are also suggested.

/pdf/classification-algorithms-for-knowledge-mapping-of-expert-in-236vpjkvq5.pdf

Classification algorithms for knowledge mapping of expert in Energy industry

Intellectual Capital (IC) is considered as an intangible asset of an organization and further it is used as the measure of organizational assets in some organizations in Europe and Australia. Nevertheless, the study of IC was not extensively investigated in Thailand. The objective of this paper was to apply a data mining technique, association rule to mine data collected from target organizations. In this study, public and private organizations located in the central part of Thailand were surveyed on their Intellectual Capital characteristics. These target organizations were divides into three classes: low, average, high according to their IC scores. Then, association rule algorithm, Apriori was applied to find the relationships of 24 attributes in each IC Class. Results of association rule mining were reported. The implication of this model was also suggested.

Association Rule Mining for Intellectual Capital of Enterprises in Central Region of Thailand

At present, there are many online learning systems which are available and provided on the Internet Almost all of these systems are static contents and test banks To improve the teaching and learning on the Internet, this study proposes a new learning system that can be adjusted to the knowledge and understanding of students This system uses K-Means Algorithm to cluster the student by pretest scores Then, the lessons are provided for each appropriate clusters The new learning system is developed to provide lessons according to students knowledge and performance This will enhance self leaning on the Internet and it will save time for learning in the future

http://www.ijcim.th.org/past_editions/2004V12N2(SP)/pdf/p224-232-SUp-online%20contentbased.pdf

Online Content-Based Learner System

This research presents approaches for combined classification by using attribute filtering with data classification The performance comparisons of single and combined classification algorithms were used for classifying the conduct disorder of vocational students in Thailand Single classification included the performance comparisons of four classifiers: 1) Naive Bayes 2) Bayesian Belief Network 3) C45 algorithm and 4) RIPPER algorithm Combined classification included two steps: attributes filtration and data classification First step was the attribute selection technique named genetic search Then, results were assessed by using three evaluators: 1) Correlation-based Feature Selection (CFS) 2) Consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation Second step was the classification of data set by using selected attributed from the first step and four classification algorithms used for single classification Next, the measurements of classification accuracy had been performed by using the k-fold Cross Validation technique for both single and combined classifications The outperformed classification model from single and combined classifications was identified The model was named Conduct Disorder Classification Model (CDCM) It was found that combined classification using genetic search and wrapper subset evaluator with Decision Tree (C45) algorithm, had the highest accuracy rate at 8301% As well, results from CDCM evaluation showed that factors associated with conduct disorders of students included (1) grade point average of high school, (2) gender, (3) age, (4) father income, (5) grade of typing I subject, (6) height, (7) blood group and (8) grade of business subject Future works were also suggested

Anongnart Srivihok

Papers

Classification algorithms for knowledge mapping of expert in Energy industry

Association Rule Mining for Intellectual Capital of Enterprises in Central Region of Thailand

Online Content-Based Learner System

Combined Algorithms for Classification of Conduct Disorder of Students in Thai Vocational School

SWIS: Web-based Intelligent System of Intellectual Capital for SME in Thailand