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A Framework for a decision tree learning algorithm with K-NN 1

13th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT)

作者： Kurematsu, Masaki Hakura, Jun Fujita, Hamido Iwate Prefectual Univ Fac Software & Informat Sugo 152-52 Takizawa Iwate 0200611 Japan

ISBN: (数字)9783319175300

ISBN: (纸本)9783319175300;9783319175294

In this paper, we proposed a modified decision tree learning algorithm. We tried to improve the conventional decision tree learning algorithm. There are some approaches to do it. These methods have a modified learning phase and a decision tree made by them includes some new attributes and/or class label gotten by modified process. As a result, It is possible that exists modified decision tree learning algorithm degrade of the comprehensibility of a decision tree. So we focus on the prediction phase and modified it. Our proposed approach makes a binary decision tree based on ID3, which is one of well-known conventional decision tree learning algorithms and predicts the class label of new data items based on K-NN instead of the algorithm used in ID3 and most of the conventional decision tree learning algorithm. Most of the conventional decision tree learning algorithms predicts a class label based on the ratio of class labels in a leaf node. They select the class label which has the highest proportion of the leaf node. However, when it is not easy to classify dataset according to class labels, leaf nodes includes a lot of data items and class labels. It causes to decrease the accuracy rate. It is difficult to prepare good training dataset. So we predict a class label from k nearest neighbor data items selected by K-NN in a leaf node. We implemented three programs. First program is based on our proposed approach. Second program is based on the conventional decision tree learning algorithms and third program is based on K-NN. In order to evaluate our approach, we compared these programs using a part of open datasets from UCL learning repository. Experimental result shows our approach is better than others.

关键词： decision tree learning algorithm K-NN ID3 Min-max normalization z-score standardization The machine learning repository

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A Framework for Improvement a decision tree learning algorithm Using K-NN 13

A Framework for Improvement a Decision Tree Learning Algorit...

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13th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT)

作者： Kurematsu, Masaki Hakura, Jun Fujita, Hamido Iwate Prefectual Univ Fac Software & Informat Takizawa Iwate Japan

ISBN: (纸本)9781614994343;9781614994336

In this paper, we proposed a modified decision tree learning algorithm. In order to improve the traditional decision tree learning algorithm, we modified a predict phase though exists approached modified a learning phase. Our proposed approach makes a decision tree by a traditional decision tree learning algorithm and predicts new data items' class label by K-NN. The traditional decision tree learning algorithm predicts a class label based on the ratio of class labels in a leaf node. When it is not easy to classify data set according to class labels, leaf nodes includes a lot of data items and class labels. It causes to decrease the accuracy rate. However, it is difficult to prepare good training data set. So we used K-NN to predict a class label from data items in a leaf node. In order to evaluate our approach, we did an experiment using a part of open data sets from UCL learning repository. We compared our approach to ID3 which is one of traditional decision tree learning algorithms and K-NN in this experiment. Experimental result shows our approach is better than ID3 when the leaf nodes include a lot of data items. When the leaf nodes include some data items, our approach can perform like as ID3. So we can say that our approach is useful to modify a decision tree learning algorithm. We don't change a learning process so that our approach doesn't change the readability of a decision tree. In addition to, our approach is better than K-NN. We think that a decision tree works for K-NN as data cleaning. It says that our approach is useful for K-NN. Though we can show the advantage of our approach according to the experiment, there are some data items we can not predict correctly. In future, we have to evaluate experimental results and process in detail. We have to ascertain the cause of error. And we consider how to modify our approach to correct errors. It is likely that normalization is one of useful method. In addition to, we have to evaluate our new approach using

关键词： decision tree learning algorithm K-NN ID3

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Fall detection system for elderly people using IoT and Big Data 9th

Fall detection system for elderly people using IoT and Big D...

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9th International Conference on Ambient Systems, Networks and Technologies (ANT) / 8th International Conference on Sustainable Energy Information Technology (SEIT)

作者： Yacchirema, Diana Suarez de Puga, Jara Palau, Carlos Esteve, Manuel Univ Politecn Valencia Camino Vera S-N Valencia 46022 Spain Escuela Politec Nacl Ladron de Guevara E11-253 Quito 17012759 Ecuador

Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elder and a significant decrease in his mobility, independence and life quality. In that sense, the present work proposes an innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of low-power wireless sensor networks, smart devices, big data and cloud computing. For this purpose, a 3D-axis accelerometer embedded into a 6LowPAN device wearable is used, which is responsible for collecting data from movements of elderly people in real-time. To provide high efficiency in fall detection, the sensor readings are processed and analyzed using a decision trees-based Big Data model running on a Smart IoT Gateway. If a fall is detected, an alert is activated and the system reacts automatically by sending notifications to the groups responsible for the care of the elderly people. Finally, the system provides services built on cloud. From medical perspective, there is a storage service that enables healthcare professional to access to falls data for perform further analysis. On the other hand, the system provides a service leveraging this data to create a new machine learning model each time a fall is detected. The results of experiments have shown high success rates in fall detection in terms of accuracy, precision and gain. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Fall detection Internet-of-Things Big Data 6LowPAN wearable sensor Smart IoT Gateway fall detection decision tree learning algorithm accelerometer elderly people

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Fall detection system for elderly people using IoT and Big Data

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Procedia Computer Science 2018年 130卷 603-610页

作者： Diana Yacchirema Jara Suárez de Puga Carlos Palau Manuel Esteve Universitat Politècnica de València Camino de Vera S/N Valencia 46022 Spain Escuela Politécnica Nacional Ladrón de Guevara E11-253 Quito 17-01-2759 Ecuador

关键词： Fall detection Internet-of-Things Big Data 6LowPAN wearable sensor Smart IoT Gateway fall detection decision tree learning algorithm accelerometer elderly people

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learning user preferences of route choice behaviour for adaptive route guidance

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IET INTELLIGENT TRANSPORT SYSTEMS 2007年第2期1卷 159-166页

作者： Park, K. Bell, M. Kaparias, I. Bogenberger, K. Univ London Imperial Coll Sci Technol & Med Ctr Transport Studies Dept Civil & Environm Engn London SW7 2AZ England BMW AG Corp Qual Prod Monitoring D-80788 Munich Germany

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction.

关键词： data mining computerised navigation Traffic engineering computing user modelling user model learning (artificial intelligence) driver information systems decision tree learning algorithm Knowledge engineering techniques user preferences Combinatorial mathematics adaptive route guidance User interfaces decision trees user satisfaction navigation systems route choice behaviour

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