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Decision Tree induction is commonly used classification algorithm. One of the important problems is how to use records with unknown values from training as well as testing data. Many approaches have been proposed to address the impact of unknown values at training on accuracy of prediction. However, very few techniques are there to address the problem in testing data. In our earlier work, we discussed...
The hysteresis model with advantages of easy implementation and high identification accuracy is highly desired in the numerical electromagnetic-field simulations, compensation and control of smart actuators, etc.
Data pre-processing for machine learning methods is key step for knowledge discovery process. Depending on nature of the data, pre-processing might take the majority time of data analysis. Correctly prepared data for processing guarantees precise and reliable results of data analysis. This paper analyses initial data pre-processing influence to attack detection accuracy by using Decision Trees, Naïve...
B-cell epitope is the small portion of antigen surface that is identified by antibodies. Epitope prediction is the task of identification of antigen surface in one of the classes of epitopes and non-epitopes. The prediction of B-cell epitopes is affected by different scales (features) of amino acid samples, such as hydrophobicity, polarity and flexibility, and so, it is necessary to utilize an appropriate...
The development of algorithms and models to be used for prediction of the reliability and health monitoring of components and sensors is of great importance in aerospace, automotive and power generation industry. For this purpose metamodels have been developed that are based on physical simulations and that are able to quantify the impact of uncertainties on system behavior. These surrogate metamodels...
Developers spend a significant amount of their time exploring source code. Yet, little is known about the way developers break down their code exploration or the fine-grained navigation for change tasks within methods. The objective of our research is to address this gap and learn more about developers' code navigation for change tasks to devise better tool support. For our research, we perform exploratory...
This study evaluates the relationship between near infrared light and glucose concentration by means of adaptive linear neuron. Firstly, the design and the development of the proposed glucose measurement device are presented. After that, the experiment design of acquiring sufficient near infrared data for training and testing is described. Next, adaptive linear neuron was trained and validated to...
Empirical studies have shown that most software interaction faults involve one or two variables interacting, with progressively fewer triggered by three to six variables interacting. This paper introduces a model for the origin of this distribution. We start with two empirically reasonable assumptions regarding the distribution of branch conditions in code and the proportion of t-way combinations...
Stochastic simulations are developed and employed across many fields, to advise governmental policy decisions and direct future research. Faulty simulation software can have serious consequences, but its correctness is difficult to determine due to complexity and random behaviour. Stochastic simulations may output a different result each time they are run, whereas most testing techniques are designed...
One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve...
Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved...
In recent years, it has been shown that fault prediction models could effectively guide test effort allocation in finding faults if they have a high enough fault prediction accuracy (Norm(Popt) > 0.78). However, it is often difficult to achieve such a high fault prediction accuracy in practice. As a result, fault-prediction-model-guided allocation (FPA) methods may be not applicable in real development...
Fitbit devices are one of the most popular wearable activity monitors in the consumer market. They are considerably cheaper than many of their clinical grade counterparts. However, they utilize proprietary algorithms for estimation of physical activity (PA). This study aims to model the measures of PA as reported by the ActiGraph GT3X using Fitbit measures of steps, METs, and intensity level. Such...
Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. Learning to rank is useful for document retrieval, it uses feature vector to rank, but there is no feature about document topic. Our paper combines LDA and learning to rank, adds a topic feature into the feature vector of learning...
In order to avoid the problems in traditional forecasting methods which demand too much of various data types and have difficulty in training models, this paper proposes two rapid prediction methods which are called “One by One Comparison” and “Regression as a Whole”. By using the two methods, as long as you get playing index in the first few days of a TV drama, the total playing index accumulation...
Medical records of Traditional Chinese Medicine (TCM) are usually free text and unstructured data, how to extract medical terms from TCM medical records based on conditional random fields is an interesting problem. TCM medical records obtained from dermatology in Guangdong Provincial Hospital of Chinese Medicine are segmented to single words and labeled with grammatical properties of words by TCM...
This paper presents Artificial Neural Network (ANN) technique for predicting the output power from Grid-Connected Photovoltaic (GCPV) system. Different inputs are utilized in several models of ANN in order to obtain the output power. ANN parameters are chosen using trial-and-error method to find the optimal value of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation...
Multi-script writer identification consists in identifying a person of a given text written in one script from the samples of the same person written in another script. The rationale behind this is that the writing style of an individual remains constant across different scripts. While this hypothesis may hold, recent results on a multi-script writer identification competition show that classical...
An efficient wind speed forecasting algorithm based on the efficient Polynomial kernel ridge extreme learning machine is proposed in this paper. This algorithm can be defined as PK-RELM. The effectiveness of this proposed algorithm has been validated in this paper by comparing it with sigmoid kernel (SK-RELM) model. In order to compute the output weight vector in chunks and to improve the stability...
The idea that a concept is properly learned by an agent when the agent is able to generate examples and non-examples of the concept, has motivated research on generative models. Generative models are trained with the aim of improving performance of tasks such as classification. In this paper, a Long Short Term Memory (LSTM) architecture for simultaneous generation-classification is presented. The...
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