Coke Index Analysis Based on Principal Component Analysis and Decision Tree Mining

Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision tree mining to analyze the relations between blast furnace production index and coke index . The materials of blast furnace production mainly include ore, coke and coal, in which the coke quality index have the biggest influence on the blast furnace production index. It has profound meaning to analyze the relation between coke index and blast furnace production index to evaluate Coke quality indicators reasonably[1] and improve the blast furnace production index. Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method[2]，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision-tree-based data-mining to analyze the relations between blast furnace production index and coke index. On the one hand this method can get few representative indexes from so many evaluation indexes by principal component analysis; on the other hand, decision-tree-based data-mining on the coke representative index based on the principal component analysis can get accurately quantitative relation between blast furnace production index and coke index.

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Data Mining in Analysis of Biomechanical Signals

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.147-149.588 ◽

2009 ◽

Vol 147-149 ◽

pp. 588-593 ◽

Cited By ~ 3

Author(s):

Marcin Derlatka ◽

Jolanta Pauk

Keyword(s):

Data Mining ◽

Principal Component Analysis ◽

Cerebral Palsy ◽

Spina Bifida ◽

Decision Tree ◽

Principal Component ◽

Data Preprocessing ◽

Component Analysis ◽

Kernel Principal Component Analysis

In the paper the procedure of processing biomechanical data has been proposed. It consists of selecting proper noiseless data, preprocessing data by means of model’s identification and Kernel Principal Component Analysis and next classification using decision tree. The obtained results of classification into groups (normal and two selected pathology of gait: Spina Bifida and Cerebral Palsy) were very good.

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New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

10.5772/intechopen.96561 ◽

2021 ◽

Author(s):

Anwar Yahya Ebrahim ◽

Hoshang Kolivand

Keyword(s):

Principal Component Analysis ◽

Decision Tree ◽

Discrete Cosine Transform ◽

Principal Component ◽

Component Analysis ◽

Extraction System ◽

Decision Tree Classifier ◽

Tree Classifier ◽

The World ◽

Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

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Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

Remote Sensing ◽

10.3390/rs13204123 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4123

Author(s):

Hanqi Wang ◽

Zhiling Wang ◽

Linglong Lin ◽

Fengyu Xu ◽

Jie Yu ◽

...

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Component Analysis ◽

Evaluation Index ◽

Evaluation Indexes ◽

Learning Network ◽

Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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Food Grain Production Index Using Principal Component Analysis in Karnataka State

International Journal of Current Microbiology and Applied Sciences ◽

10.20546/ijcmas.2019.801.335 ◽

2019 ◽

Vol 8 (01) ◽

pp. 3138-3143

Author(s):

N.L. Pavithra ◽

K.V. Ashalatha ◽

J. Megha ◽

G.R. Manjunath ◽

Siddu Hanabar

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Grain Production ◽

Production Index ◽

Karnataka State ◽

Food Grain

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A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

Journal of Computing and Information Technology ◽

10.20532/cit.2019.1004591 ◽

2020 ◽

Vol 27 (4) ◽

pp. 1-16

Author(s):

Meenal Jain ◽

Gagandeep Kaur

Keyword(s):

Principal Component Analysis ◽

Intrusion Detection ◽

Decision Tree ◽

Principal Component ◽

Component Analysis ◽

Feature Reduction ◽

Support Vector ◽

Detection Techniques ◽

Difference Measure ◽

Reduction Techniques

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.

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Instance selection and feature extraction using cuttlefish optimization algorithm and principal component analysis using decision tree

Cluster Computing ◽

10.1007/s10586-018-1821-z ◽

2018 ◽

Vol 22 (S1) ◽

pp. 89-101 ◽

Cited By ~ 6

Author(s):

M. Suganthi ◽

V. Karunakaran

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Decision Tree ◽

Optimization Algorithm ◽

Principal Component ◽

Component Analysis ◽

Instance Selection

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Penerapan Principal Component Analysis untuk Peningkatan Kinerja Algoritma Decision Tree pada Iris Dataset

Indonesian Journal of Technology, Informatics and Science (IJTIS) ◽

10.24176/ijtis.v1i2.4939 ◽

2020 ◽

Vol 1 (2) ◽

pp. 55-58

Author(s):

Putri Kurnia Handayani

Keyword(s):

Data Mining ◽

Principal Component Analysis ◽

Decision Tree ◽

Supervised Learning ◽

Principal Component ◽

Component Analysis

Data mining merupakan salah bidang ilmu yang bermanfaat untuk pengenalan pola/knowledge yang tersimpan dalam database. Klasifikasi merupakan salah satu peran dalam bidang data mining. Termasuk ke dalam supervised learning, klasifikasi digunakan untuk memprediksi objek yang belum memiliki kelas/label. Penggunaan algoritma decision tree untuk proses mining dataset bunga iris dikarenakan kemudahan dalam representasi knowledge yang dihasilkan. Selain itu, decision tree juga termasuk ke dalam eager learner sehingga akurasi dari knowledge yang dihasilkan lebih baik. Penggunaan principal component analysis (PCA) dalam optimasi algoritma decision tree, dilakukan saat preprocessing dataset. PCA berfungsi untuk mereduksi dimensi, fitur yang saling berkorelasi akan dipertahankan. Penggunaan dataset publik bunga iris diambil dari UCI Repository. Berdasarkan hasil perhitungan, akurasi algoritma decision tree setelah dilakukan optimasi dengan PCA terhadap dataset bunga iris sebesar 95.33%.

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