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

This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and single classification. Hybrid classification includes two steps, step one is attribute selection by search method using genetic search and results are compared by three evaluators: 1) Correlation-based Feature Selection (CFS) 2) Consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation. Step two is the classification of data set by using selected attributed from step one and four classification algorithms. Next, Simple classification used classification algorithms only without attribute selection. The four classification algorithms that used in this experiment for comparing in two methods are : 1) Naive Bayes classifier 2) Baysian Belief Network 3) C4.5 algorithm and 4) RIPPER algorithm. The measurements of classification efficiency had been obtained by using the k-fold cross validation technique. From the experiment, it was found that hybrid classification technique using genetic search and CFS evaluator with C4.5 algorithm, gives the highest accuracy rate at 82.52%. However, results from F-measure evaluation showed that C4.5 algorithm did not fit for all data types. The hybrid classification technique using genetic search and wrapper subset with Baysian belief network can give a better precision value which can be seen in the F-measure, and it gives the accuracy rate at 82.42%.

Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students

Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach’s effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

/pdf/a-novel-method-for-credit-scoring-based-on-cost-sensitive-1aheh8p9m5.pdf

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

This paper presents the personalized recommendation system for e-tourism by using statistic technique base on Bayes Theorem to analyze user behaviors and recommend trips to specific users. The system is evaluated by using Recall, Precision and F-measure. Results demonstrate that it is possible to develop Personalization Recommendation System. Past and recent information of customer’s behaviors are used for recommending products and services best fit to relevant customers in the e-Tourism.

/pdf/personalized-trip-information-for-e-tourism-recommendation-asr27tbd2c.pdf

Personalized Trip Information for E-Tourism Recommendation System Based on Bayes Theorem

The objective of this study is to propose a model for planning course registration by using a data mining technique: Bayesian network. The proposed model can be used to predict the sequences of courses to be registered by undergraduate students whose majors are computer science or engineering. The data set was obtained from student enrollments and include GPA and grades in each subject for first and second year students from a private university in Thailand. Evaluations show that the predictive power of this model is acceptable. The implications from this studypsilas findings suggest that the model can be applied for advising students in planning courses to be registered in each semester. Further, the model appears to be useful for improving curriculum development in order to fit both studentspsila and university requirements.

Using Bayesian Network for planning course registration model for undergraduate students

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 to fit potential customers. In this study, a Personalization Travel Support System is introduced to manage traveling information for user. It provides the information that matches the users’ interests. This system applies the Reinforcement Learning to analyze, learn customer behaviors and recommend products to meet customer interests. There are two learning approaches using in this study. First, Personalization Learner by Group Properties is learning from all users in one group to find the group 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 Personalization Travel 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 useful information regarding the areas of personalization of web support system.

http://ra.ethz.ch/cdstore/www2005-ws/workshop/wf05/paper12.pdf

Anongnart Srivihok

Papers

Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

Personalized Trip Information for E-Tourism Recommendation System Based on Bayes Theorem

Using Bayesian Network for planning course registration model for undergraduate students

Intelligent Agent for e-Tourism: Personalization Travel Support Agent using Reinforcement Learning