Byonghwa Oh

Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm

We introduce a new adaptive genetic method for AVT generation, MCM-AVT-Learner. The MCM-AVT-Learner imports the mutation and crossover matrices which makes effective use of the fitness ranking and loci statistics information. The suggested method is not only parameter-free, but also capable of producing high quality AVTs. We describe experiments on several complete and missing benchmark data sets that compare the performance of AVT-DTL using the reslut AVTs of the MCM-AVT-Learner and existing AVT learning algorithms. Results show that the AVTs generated by MCM-AVT-Learner are competitive with human-generated AVTs or AVTs generated by HAC-AVT-Learner and GA-AVT-Learner in terms of classification accuracy and the compactness of the classifier.

Prioritized Traffic Information Delivery Based on Historical Data Analysis

The main objective of this paper is to present and evaluate methods that can be used to extract valuable information from traffic data history for use in travel information and guidance systems. Through two different analyses, we identify the roads which the system should pay closer attention to. First, we introduce an approach that finds the patterns of the traffic speed which lie within the data history. Second, we discover the relations between traffic speeds and various features of roads. The result of our study let travel guidance systems know how heavy the traffic will be at any given road. We extend these approaches to tackle the problem of incident management, and designed a system that returns, the expected range of effect of an incident using road features.

A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying an instance, we apply a weighted voting scheme among the classifiers that include the instance in their region. We used 11 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as RBE, bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed other approaches.

Distributed genetic algorithm using automated adaptive migration

We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithm is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We apply our distributed genetic algorithm to the feature subset selection task which has been one of the active research topics in machine learning. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets.

RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

AbstractWe present a novel approach for collaborative filtering, RLCF, that considers the dynamics of user ratings. RLCF is based on reinforcement learning applied to the sequence of ratings. First, we formalize the collaborative filtering problem as a Markov Decision Process. Then, we learn the connection between the temporal sequences of user ratings using Q-learning. Experiments demonstrate the feasibility of our approach and a tight relationship between the past and the current ratings. We also suggest an ensemble learning in RLCF and demonstrate its improved performance.

Ensemble Learning of Regional Classifiers

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying new data, we apply a weighted voting among the classifiers that include the data in their regions. We used 10 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.

Graph Construction Based on Fast Low-Rank Representation in Graph-Based Semi-Supervised Learning

According to the present invention, a method for constructing a graph in graph-based semi-supervised learning comprises the steps: (a) receiving a data set (X) and a nearest neighbor number (k); (b) generating a k-NN graph using the data set (X), and then calculating L; (c) decomposing X using SkinnySVD; (d) obtaining βL using SVD; (e) updating J, W, and Q while satisfying a predetermined condition; and (f) obtaining an optimal solution by calculating Y_1 and Y_2, such that it is possible to construct a graph based on a fast low-rank representation algorithm. According to the present invention, the method for constructing a graph is based on a fast low-rank representation and configured to introduce additional constraints to an underlying optimization goal and optimize the underlying optimization goal such that it is possible to quickly acquire a better solution.

Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning

Graph-based semi-supervised learning has recently come into focus for to its two defining phases: graph construction, which converts the data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. And the label inference is based on the smoothness assumption of semi-supervised learning. In this study, we propose an enhanced label inference approach which incorporates the importance of each vertex into the existing inference algorithms to improve the prediction capabilities of the algorithms. We also present extensions of three algorithms which are capable of taking the vertex importance variable to apply in learning. Experiments show that our algorithms perform better than the base algorithms on a variety of datasets, especially when the data is less smooth over the graphs.

A Label Inference Algorithm Considering Vertex Importance in Semi-Supervised Learning

Abstract Semi-supervised learning is an area in machine learning that employs both labeled and unlabeled data in order to train a model and has the potential to improve prediction performance compared to supervised learning. Graph-based semi-supervised learning has recently come into focus with two phases: graph construction, which converts the input data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. The inference is based on the smoothness assumption feature of semi-supervised learning. In this study, we propose an enhanced label inference algorithm by incorporating the importance of each vertex. In addition, we prove the convergence of the suggested algorithm and verify its excellence.

A Reinforcement Learning Approach to Collaborative Filtering Considering Time-sequence of Ratings

In recent years, there has been increasing interest in recommender systems which provide users with personalized suggestions for products or services. In particular, researches of collaborative filtering analyzing relations between users and items has become more active because of the Netflix Prize competition. This paper presents the reinforcement learning approach for collaborative filtering. By applying reinforcement learning techniques to the movie rating, we discovered the connection between a time sequence of past ratings and current ratings. For this, we first formulated the collaborative filtering problem as a Markov Decision Process. And then we trained the learning model which reflects the connection between the time sequence of past ratings and current ratings using Q-learning. The experimental results indicate that there is a significant effect on current ratings by the time sequence of past ratings.