Jin-Hyuk Hong

Location-based recommendation system using Bayesian user's preference model in mobile devices

As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to users physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects users preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.

Understanding and prediction of mobile application usage for smart phones

It is becoming harder to find an app on ones smart phone due to the increasing number of apps available and installed on smart phones today. We collect sensory data including app use from smart phones, to perform a comprehensive analysis of the context related to mobile app use, and build prediction models that calculate the probability of an app in the current context. Based on these models, we developed a dynamic home screen application that presents icons for the most probable apps on the main screen of the phone and highlights the most probable one. Our models outperformed other strategies, and, in particular, improved prediction accuracy by 8% over Most Frequently Used from 79.8% to 87.8% (for 9 candidate apps). Also, we found that the dynamic home screen improved accessibility to apps on the phone, compared to the conventional static home screen in terms of accuracy, required touch input and app selection time.

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming

OBJECT The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.

Efficient huge-scale feature selection with speciated genetic algorithm

With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in those fields. Since the features of data obtained by microarray technology come to be over thousands, it is essential to extract useful information by selecting proper features. The information without any feature selection might be redundant so that this can deteriorate the performance of classification. The conventional feature selection method with genetic algorithm has difficulty for huge-scale feature selection. In this paper, we modify the representation of chromosome to be suitable for huge-scale feature selection and adopt speciation to enhance the performance of feature selection by obtaining diverse solutions. Experimental results with DNA microarray data from cancer patients show that the selected genes by the proposed method are useful for cancer classification.

Lymphoma cancer classification using genetic programming with SNR features

Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification rules obtained. Since gene expression data have thousands of features, it is nearly impossible to represent and understand their complex relationships directly. In this paper, we adopt the SNR (Signal-to-Noise Ratio) feature selection to reduce the dimensionality of the data, and then use genetic programming to generate cancer classification rules with the features. In the experimental results on Lymphoma cancer dataset, the proposed method yielded 96.6% test accuracy in average, and an excellent arithmetic classification rule set that classifies all the samples correctly is discovered by the proposed method.

A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification

Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification with effective decomposition and reconstruction schemes. Decomposition schemes such as one-vs.-rest (OVR) and pair-wise partition a dataset into several subsets of two classes so as to produce multiple outputs that should be combined. Majority voting or winner-takes-all is a representative reconstruction scheme to combine those outputs, but it often causes some problems to consider tie-breaks and tune the weights of individual classifiers. In this paper, we propose a novel method in which SVMs are generated with the OVR scheme and probabilistically ordered by using the naive Bayes classifiers (NBs). This method is able to break ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on several popular multi-class cancer datasets and produced higher accuracy than conventional methods.

Understanding physiological responses to stressors during physical activity

With advances in physiological sensors, we are able to understand peoples physiological status and recognize stress to provide beneficial services. Despite the great potential in physiological stress recognition, there are some critical issues that need to be addressed such as the sensitivity and variability of physiology to many factors other than stress (e.g., physical activity). To resolve these issues, in this paper, we focus on the understanding of physiological responses to both stressor and physical activity and perform stress recognition, particularly in situations having multiple stimuli: physical activity and stressors. We construct stress models that correspond to individual situations, and we validate our stress modeling in the presence of physical activity. Analysis of our experiments provides an understanding on how physiological responses change with different stressors and how physical activity confounds stress recognition with physiological responses. In both objective and subjective settings, the accuracy of stress recognition drops by more than 14% when physical activity is performed. However, by modularizing stress models with respect to physical activity, we can recognize stress with accuracies of 82% (objective stress) and 87% (subjective stress), achieving more than a 5-10% improvement from approaches that do not take physical activity into account.

ConaMSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors

With the growth on the concern about context-aware applications, it becomes important to recognize and share user context. Even though there are some applications, it is still limited in managing simple contexts. In this paper, we propose a context-aware messenger application that exploits dynamic Bayesian networks to automatically infer a users context and shares contextual information to enrich electronic communication. It collects various sensory information and displays representative user contexts such as emotion, stress, and activity in the form of icons in the messenger program.

Evolution of emergent behaviors for shooting game characters in Robocode

Various digital characters, which are automatic and intelligent, are attempted with the introduction of artificial intelligence or artificial life. Since a characters behavior is designed by a developer, the style can be static and simple. Even complex patterns designed by a developer cannot satisfy various users and easily make them feel tedious. A game should maintain various and complex characters behaviors, but it is not easy for the developer to design them. In this paper, we adopt genetic algorithm to produce various and excellent behavior-styles for characters especially focusing on Robocode which is one of the promising simulators for artificial intelligence.

A semantic Bayesian network approach to retrieving information with intelligent conversational agents

As access to information becomes more intensive in society, a great deal of that information is becoming available through diverse channels. Accordingly, users require effective methods for accessing this information. Conversational agents can act as effective and familiar user interfaces. Although conversational agents can analyze the queries of users based on a static process, they cannot manage expressions that are more complex. In this paper, we propose a system that uses semantic Bayesian networks to infer the intentions of the user based on Bayesian networks and their semantic information. Since conversation often contains ambiguous expressions, the managing of context and uncertainty is necessary to support flexible conversational agents. The proposed method uses mixed-initiative interaction (MII) to obtain missing information and clarify spurious concepts in order to understand the intention of users correctly. We applied this to an information retrieval service for websites to verify the usefulness of the proposed method.