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.

https://sci2s.ugr.es/sites/default/files/files/TematicWebSites/sicidm/2010-Garcia-INS.pdf

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

mental representations a dual coding approach is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the mental representations a dual coding approach is universally compatible with any devices to read.

Mental Representations A Dual Coding Approach

We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn++.NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn++ family of algorithms, that is, without requiring access to previously seen data. Learn++.NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn++.NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper.

/pdf/incremental-learning-of-concept-drift-in-nonstationary-39gk79d4v5.pdf

Incremental Learning of Concept Drift in Nonstationary Environments

https://ir.nctu.edu.tw:443/bitstream/11536/9729/1/000251621700009.pdf

A discretization algorithm based on Class-Attribute Contingency Coefficient

Evidence indicates that concept mapping can reduce students' cognitive load and facilitate meaningful learning. Using a concept map description approach, the ontology defines the relationship between concepts that is feasible for resource sharing and reusing. In this study, we implement a concept map learning system with ontology technology to help users search the concept map, determine relationships between nodes or predicates, and find the common concept or predicate among the concepts to help reduce the user's cognitive load.

Ontology technology to assist learners' navigation in the concept map learning system

In a database, the concept of an example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using the old dataset is not suitable for predicting a new dataset. Therefore, the problem of concept drift has attracted a lot of attention in recent years. Although many algorithms have been proposed to solve this problem, they have not been able to provide users with a satisfactory solution to concept drift. That is, the current research about concept drift focuses only on updating the classification model. However, real life decision makers might be very interested in the rules of concept drift. For example, doctors desire to know the root causes behind variation in the causes and development of disease. In this paper, we propose a concept drift rule mining tree, called CDR-Tree, to accurately discover the underlying rule governing concept drift. The main contributions of this paper are: (a) we address the problem of mining concept-drifting rules which has not been considered in previously developed classification schemes; (b) we develop a method that can accurately mine rules governing concept drift; (c) we develop a method that should classification models be required, can efficiently and accurately generate such models via a simple extraction procedure rather than constructing them anew; and (d) we propose two strategies to reduce the complexity of concept-drifting rules mined by our CDR-Tree.

http://ir.ncue.edu.tw/ir/bitstream/987654321/8861/1/2020102110003.pdf

Mining decision rules on data streams in the presence of concept drifts

Current mathematics education emphasizes techniques, formulas, and procedures, neglecting the importance of understanding, presentation, and reasoning. This turns students into passive listeners that are well-practiced only in using formulas that they do not understand. We therefore adopted the Polya problem-solving method to provide students with a diversified foundation for problem-solving. Furthermore, giving students immediate feedback in the form of prompts can help them to find the answers on their own them, thereby helping them learn more effectively. The primary objective of this study was to investigate the influences of a teaching activity incorporating Polya’s method and an appropriate prompt mechanism on the learning effectiveness of students. Research Subjects were two classes selected from an elementary school in Taiwan; one as the experimental group were taught with the providing appropriate prompts based on the Polya strategy of mathematical problems solving, the other one as the control group were treated by the conventional instructions. The results showed that: (1) there were significant differences between the experimental group and the control group in the learning effectiveness; (2) most of the students in the experimental group were satisfied by the proposed prompts system.

/pdf/an-appropriate-prompts-system-based-on-the-polya-method-for-nrvprfctpz.pdf

An Appropriate Prompts System Based on the Polya Method for Mathematical Problem-Solving

The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database.

https://ir.nctu.edu.tw:443/bitstream/11536/9352/1/000253521900080.pdf

Chien-I Lee

Papers

A discretization algorithm based on Class-Attribute Contingency Coefficient

Ontology technology to assist learners' navigation in the concept map learning system

Mining decision rules on data streams in the presence of concept drifts

An Appropriate Prompts System Based on the Polya Method for Mathematical Problem-Solving

An approach to mining the multi-relational imbalanced database