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In data mining and machine learning, many real-world problems such as bio-data classification and biomarker detection, image analysis, text mining often involve a large number of features/attributes. However, not all the features are essential since many of them are redundant or even irrelevant, and the useful features are typically not equally important. Using all the features for classification...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
The new product development and operational marketing literature grapples with incorporation of uncertainty on the market and technological structure discovered over time. In contrast, market and technological uncertainty is at the heart of neo-Schumpeterian agent-based models used in evolutionary innovation economics. We present a novel agent-based model in which designer agents design products to...
Many real-world datasets suffer from the problem of missing values. Imputation which replaces missing values with plausible values is a major method for classification with data containing missing values. However, powerful imputation methods including multiple imputation are usually computationally intensive for estimating missing values in unseen incomplete instances. Rule-based classification algorithms...
To automatically design improvements of stochastic numerical optimization algorithms is challenging due to the high computation time required to ensure sufficiently rigorous evaluation of synthesized programs. In this paper, we develop evaluation methodology that is used with the evolutionary automatic programming system ADATE to enhance two variants of the differential evolution algorithm, namely,...
Recently, it has been shown that lexicographic orderings and time travel can be used to automate the play of Nintendo Entertainment System (NES) games. In this work, we present a method for optimizing solutions to NES games. Since many of these classic Nintendo games are NP-hard, we propose a metaheuristic algorithm that works by borrowing operators from evolutionary algorithms. By using a search...
Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate...
Deep, convolutional neural networks have become the state-of-the-art method for automatic Facial Expression Recognition (FER). Because of the small size and controlled conditions of most FER datasets, however, models can still overfit to the training dataset and struggle to generalize well to new data. We present a novel approach of using crowdsourced label distributions for improving the generalization...
Software defect prediction is one of the most attractive research topics in the field of software engineering. The task is to predict whether or not a program contains semantic bugs. Previous studies apply conventional machine learning techniques on software metrics, or deep learning on source code's tree representations called abstract syntax trees. This paper formulates an approach for software...
Nowadays, text classification (TC) becomes the main applications of NLP (natural language processing). Actually, we have a lot of researches in classifying text documents, such as Random Forest, Support Vector Machines and Naive Bayes. However, most of them are applied for English documents. Therefore, the text classification researches on Vietnamese still are limited. By using a Vietnamese news corpus,...
Identifying emotional polarization in a medical report is important in screening, acquiring and synthesizing knowledge of physicians before making a clinical decision. We consider this as a classification problem whose input is a set of sentences collected from medical articles and output is the polarization of each sentence labeled as a positive, negative or neutral one. In this paper, we propose...
Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can...
Space debris removal is currently a critical issue for space development. It has been reported that five pieces of debris should be removed each year to avoid further increasing the amount of debris in orbit. To remove multiple pieces of debris, one idea is to deliver multiple satellites that can each remove one target debris from orbit. The benefit of this approach is that target debris can be removed...
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