Data Mining Practical Machine Learning Tools and Techniques

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

C4.5: Programs for Machine Learning (書評)

Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.

Data mining in education

On-line discussion forums constitute communities of people learning from each other, which not only inform the students about their peers' doubts and problems but can also inform instructors about their students' knowledge of the course contents In fact, nowadays there is increasing interest in the use of discussion forums as an indicator of student performance In this respect, this paper proposes the use of different data mining approaches for improving prediction of students' final performance starting from participation indicators in both quantitative, qualitative and social network forums Our objective is to determine how the selection of instances and attributes, the use of different classification algorithms and the date when data is gathered affect the accuracy and comprehensibility of the prediction A new Moodle's module for gathering forum indicators was developed and different executions were carried out using real data from 114 university students during a first-year course in computer science A representative set of traditional classification algorithms have been used and compared versus classification via clustering algorithms for predicting whether students will pass or fail the course on the basis of data about their forum usage The results obtained indicate the suitability of performing both a final prediction at the end of the course and an early prediction before the end of the course; of applying clustering plus class association rules mining instead of traditional classification for obtaining highly interpretable student performance models; and of using a subset of attributes instead of all available attributes, and not all forum messages but only students' messages with content related to the subject of the course for improving classification accuracy

Predicting students' final performance from participation in on-line discussion forums

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well-known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4-6weeks of the course and trustworthy enough to be used in an early warning system.

Early dropout prediction using data mining: a case study with high school students

Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.

Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

This paper proposes to apply data mining techniques to predict school failure and dropout We use real data on 670 middle-school students from Zacatecas, Mexico, and employ white-box classification methods, such as induction rules and decision trees Experiments attempt to improve their accuracy for predicting which students might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification The outcomes have been compared and the models with the best results are shown

Predicting School Failure and Dropout by Using Data Mining Techniques

This paper proposes to apply data mining techniques to predict school failure. We have used real data about 670 middle-school students from Zacatecas, Mexico. Several experiments have been carried out in an attempt to improve accuracy in the prediction of final student performance and, specifically, of which students might fail. In the first experiment the best 15 attributes has been selected. Then two different approaches have been applied in order to resolve the problem of classifying unbalanced data by rebalancing data and using cost sensitive classification. The outcomes of each one of these approaches using the 10 classification algorithms and 10 foldcross validation are shown and compared in order to select the best approach to our problem.

http://educationaldatamining.org/EDM2011/wp-content/uploads/proc/edm2011_paper11_short_Marquez-Vera.pdf

Predicting School Failure Using Data Mining.

This paper proposes to apply data mining techniques to predict school failure and drop out. We use real data on 670 middle-school students from Zacatecas, Mexico and employ white-box classification methods such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or drop out by: firstly, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data, and using cost sensitive classification. The outcomes have been compared and the best resulting models are shown.

http://rita.det.uvigo.es/201208/uploads/IEEE-RITA.2012.V7.N3.A1.pdf

Carlos Márquez-Vera

Papers

Early dropout prediction using data mining: a case study with high school students

Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Predicting School Failure and Dropout by Using Data Mining Techniques

Predicting School Failure Using Data Mining.

Predicción del Fracaso Escolar mediante Técnicas de Minería de Datos