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Classical linear prediction methods based on least-square estimation yields radar images with high side lobes and many spurious scattering centers while singular value decomposition (SVD) truncation used to address these issues decreases the dynamic range of the image. So, radar images provided by these methods are not appropriate for classification purposes. In this work, sparsity constraints are...
Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system...
Bayesian network (BN), an important machine learning technique, has been widely used in modeling relationships among random variables. BN is considered to be suitable for tasks like prediction, classification and cause analysis. In fact, Bayesian network model often preforms better precision than other commonly used algorithm models in classification and prediction. Meanwhile, taking Max-Min-Hill-Climbing...
Extreme learning machine (ELM) for single-hidden-layer feedforward neural networks has been widely used in classification and regression for its fast learning speed. However, a single ELM suffers from problems of stability and overfitting. Ensemble approach can effectively resolve these problems. This paper proposes a selective ensemble learning algorithm based on differential evolution (DE) for classification...
The stock market is one of the most important part of the financial market. However, it is difficult for investors to extract information from data. This paper studied the analysis of the announcement during the suspension period. It offers feasible suggestions, hopefully. It established a prediction model based on the content of announcements. The prediction is about the stock price. It analyzes...
The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students...
The identification of predictive features associated with distinct medical outcomes is a key requirement for meaningful clinical decision support. Usually, their discovery is based on sets of labeled examples and an analysis of the inherent information of the features w. r. t. the target variable. However, obtaining large sets of labeled examples may be not feasible and the sole label consideration...
Quality monitoring and prediction plays a key role in improving product quality and achieving automated quality control in manufacturing processes such as the abrasion-resistant material manufacturing process. Traditional methods that rely on the use of first-principle models are difficult to formulate due to the increasing complexity and high dimensionality of manufacturing processes. Data-driven...
Symbolic data classification is of great importance in classification of massive high dimensional data that may exist in domains such as bioinformatics and web mining. Feature values (events) of symbolic data are generally not single values, as in the classical case, but rather list of values, intervals or, more generally, distributions. This study proposes a symbolic classification algorithm that...
Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then,...
Software Analytics is gaining momentum as aresult of involved empirical research in enhancing quality andproductivity of software engineering activities. There have beenrigorous research efforts in the areas of bug prediction and testingeffort prediction by making use of historical data. The problemof predicting bug fix times is an interesting problem with lotsof advantages to industry but there have...
Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers...
Data mining plays an important role in the business world and it helps to the educational institution to predict and make decisions related to the students' academic status. With a higher education, now a days dropping out of students' has been increasing, it affects not only the students' career but also on the reputation of the institute. The existing system is a system which maintains the student...
In this work, the effectiveness of the popular classification techniques k-Nearest Neighbour (kNN) algorithm is integrated with Ant Colony Optimization (ACO) to predict the likelihood of getting heart disease. The analysis has been performed in two phases. In the first phase, the kNN classification is used to classify the test data. In the second phase, the ACO is used to initialize the population...
We propose a data analysis approach to classify wine into different quality categories. A data set of white wines of 4898 observations obtained from the Minho region in Portugal was used in our analysis. As the occurrence of events in the data set was imbalanced with about 93% of the observations are from one category, we applied the Synthetic Minority Over-Sampling Technique (SMOTE) to over sample...
Machine Learning is the field of computer science that learns from data by studying algorithms and their constructions. In machine learning, predictions can be made by using certain algorithms for specific inputs. In this paper important classification and clustering algorithms are discussed which can be further applied to BE (Information Technology). Third Semester to evaluate student's performance...
While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a way that while the model has enough instances to train on, they should not over-fit the model and at the same time, it must be considered that if there are not enough instances to train on, the model would not be trained properly and would give poor results when used for...
In the current healthcare setup, the use of Machine Learning has been limited as clinicians diagnose and administer treatment manually. Supervised Learning techniques can help solve many prognostic problems and help clinicians in taking decisions pertaining to healthcare. The paper presents selected machine learning techniques that can be applied for medical data, and in particular some supervised...
A question that is often arose on career management is how to choose potential employees to become chief and achieve performance target based on employees' historical data. This research attempts to answer the question and tries to determine what factors can affect an employee to become a chief and capable to achieve performance target. To address the question, this study adopts predictive modelling...
The number of kidney disease patients, one of the worldwide public health problems, has been increased yearly. Due to the high possibility of death within a short period of time, a patient must be hospitalized and appropriately cured since the first day of being diagnosed as stage 3. This is due to the fact that the patient's stage progression depends pretty much on medical history and treatment....
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