• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1283-1291
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• 2014

In this paper, we propose forward stagewise algorithm when data are very large or coming in batches sequentially over time. In this situation, ordinary boosting algorithm for large scale data and data batch stream may be greedy and have worse performance with class noise situations. To overcome those and apply to large scale data or data batch stream, we modify the forward stagewise algorithm. This algorithm has better results for both large scale data and data batch stream with or without concept drift on simulated data and real data sets than boosting algorithms.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.131-143
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• 2004

Given a path represented by a sequence of link numbers in a graph, the vine is differentiated from the loop in a sense that any link number can be visited in the path no more than once, while more than once in the loop. The vine provides a proper idea on complicated travel patterns such as U-turn and P-turn witnessed near intersections in urban transportation networks. Application of the link label method(LLM) to the shortest Path algorithms(SPA) enables to take into account these vine travel features. This study aims at expanding the LLM to a K-th path search algorithm (KPSA), which adopts the node-based-label correcting method to find a group of K number of paths. The paths including the vine type of travels are conceptualized as drivers reasonable route choice behaviors(RRCB) based on non-repetition of the same link in the paths, and the link-label-based MPSA is proposed on the basis of the RRCB. The small-scaled network test shows that the algorithm sequence works correctly producing multiple paths satisfying the RRCB. The large-scaled network study detects the solution degeneration (SD) problem in case the number of paths (K) is not sufficient enough, and the (K-1) dimension algorithm is developed to prevent the SD from the 1st path of each link, so that it may be applied as reasonable alternative route information tool, an important requirement of which is if it can generate small number of distinct alternative paths.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.