Set Of Classification Rules Research Articles

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Unconstrained handwritten digit recognition using perceptual shape primitives

In this paper, we propose a new handwritten digit recognition method which works in a very similar way as human perception. The digit image boundary is decomposed into four salient visual primitives, namely closure, smooth curve, protrusion and straight segment by defining a set of external symmetry axis. Unlike the conventional algorithms, our low complexity shape decomposition method neither searches for curvature minima nor finds optimal parsing by using shortcut and convexity rules. Based on the spatial configuration of extracted primitives, the recognizer classifies a test digit image using a set of classification rules. The performance of our proposed recognition system is evaluated on five digit datasets of four popular scripts, Odia, Bangla, Arabic and English. The recognition accuracies on the ISI Kolkata Odia and Bangla, IITBBS Odia, CMATERdb Arabic and MNIST English digit datasets are found to be 99.02, 99.25, 99.66, 97.96 and 99.11%, respectively. The proposed method outperforms the existing recognition systems on both the Odia digit datasets and achieves comparable performance in other cases.

Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.

An adaptive rule-based classifier for mining big biological data

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.

Object-Oriented Method for Rural Residential Land Extraction in the Hilly Areas of Southern China Using RapidEye Data

Abstract: The process of rapid urbanization in China features two opposing trends: declining rural population and increasing rural residential land, especially in southern hilly areas. The extraction and analysis of residential land in rural China represents an important application for remote sensing technology. The study aimed to discover rural residential land information using RapidEye satellite imagery, taking Taihe County as the research area in the hilly region of southern China. Based on multiple experiments, classification was conducted with an optimal image segmentation scale set to 200. The object-oriented classification rule set was constructed using the customized parameters NDVI, NDWI, brightness, and length/width. The areas of residential land and other land use types were interpreted by varying the parameter values for classification rule sets. Finally, validation and accuracy evaluations were carried out. The overall accuracy of residential land interpretation is 78.40%, and producer's ac...

AUTOMATED CLASSIFICATION OF LAND COVER USING LANDSAT 8 OLI SURFACE REFLECTANCE PRODUCT AND SPECTRAL PATTERN ANALYSIS CONCEPT - CASE STUDY IN HANOI, VIETNAM

Abstract. Recently USGS released provisional Landsat 8 Surface Reflectance product, which allows conducting land cover mapping over large composed of number of image scenes without necessity of atmospheric correction. In this study, the authors present a new concept for automated classification of land cover. This concept is based on spectral patterns analysis of reflected bands and can be automated using predefined classification rule set constituted of spectral pattern shape, total reflected radiance index (TRRI) and ratios of spectral bands. Given a pixel vector B6 = {b1,b2,b3,b4,b5,b6} where b1, b2,...,b6 denote bands 2, 3, ...,7 of OLI sensor respectively. By using the pixel vector B6 we can construct spectral reflectance curve. Each spectral curve is featured by a shape, which can be described in simplified form of an analogue pattern, which is consisted of 15 digits of 0, 1 and 2 showing mutual relative position of spectral vertices. Value of comparison between band i and j is 2 if bj > bi, 1 if bj = bi and 0 if bj < bi. Simplified spectral pattern is defined by 15 digits as m1,2m1,3m1,4m1,5m1,6m2,3m2,4m2,5m2,6m3,4m3,5m3,6m4,5m4,6m5,6 where mi,j is result of comparison of reflectance between bi and bj and has values of 0, 1 and 2. After construction of SSP for each pixel in the input image, the original image will be decomposed to component images, which contain pixels with the same SRCS pattern. The decomposition can be written analytically by equation A = Σnk=1Ck where A stands for original image with 6 spectral bands, n is number of component images decomposed from A and Ck is component image. For this study, we use Landsat 8 OLI reflectance image LC81270452013352LGN00 and LC81270452015182LGN00. For the decomposition, we use only six reflective bands. Each land cover class is defined by SSP code, threshold values for TRRI and band ratios. Automated classification of land cover was realized with 8 classes: forest, shrub, grass, water, wetland, develop land, barren and others. This paper provides a preliminary research result on application of multispectral image decomposition using simplified spectral pattern for classification of land cover for Hanoi area.

MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

Abstract. This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

A fuzzy rule induction algorithm for discovering classification rules

© 2016 - IOS Press and the authors. All rights reserved. Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of crisp classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts fuzzy classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets.

The Application of GF-1 Imagery to Detect Ships on the Yangtze River

Various satellite data are currently used to detect ships on the sea surface. However, no study on the use of Gaofen-1 (GF-1) data to monitor ships on the surface of inland rivers has been reported. Therefore, we proposed a method to extract inland river-surface ships from GF-1 imagery. The Normalized Differential Water Index was calculated to enhance the contrast between water and non-water areas after the preprocessing procedure. The multi-resolution segmentation method and object-oriented classification rule sets were used to detect the ships in the image. Results show that most of the ships, whose length-to-width ratio ranges from 3.0 to 7.2, could be identified correctly regardless of their size. The results also indicate that detecting ships on inland rivers using GF-1 imagery is feasible.

A support vector machine object based image analysis approach on urban green space extraction using Pleiades-1A imagery

The use of remote sensing data for urban studies has increased along with the availability of Very High-Resolution (VHR) satellite data such as IKONOS, Quickbird, Worldview, and the Pleiades. This study aimed to evaluate the use of Pleiades-1A imagery and object based image analysis (OBIA) method to extract the information of urban green spaces in some areas of Jakarta, Indonesia. Multiresolution segmentation and spectral difference segmentation were then applied to the imagery respectively. Support Vector Machine (SVM) was performed for the classification phase, followed by an expert-knowledge refinement. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI) were derived from the imagery to help the classification process. The results showed two classes of landcover, that consists of “urban green” and “non-urban green”. The accuracy assessment was then performed using the visual interpretation followed by field measurements as reference data. By using the area-based similarity measurement framework, this study scored 86 % for overall accuracy. The similarity measurement showed values above 87 % for all 20 samples. This study found that the proposed methods gave a more into “similar” results to the reference data, than the “dissimilar”. The segmentation and classification rule set built in this study still need further study to see how effective the proposed method when applied to different cities with a different landuse/landcover characteristic.

Tropical Agrarian Landscape Classification using high-resolution GeoEYE data and segmentationbased approach

We examine the use of high spatial resolution ‘GeoEYE’ imagery for land use classification in a tropical landscape. Image objects (I-Os) derived from features identification provide a basis for segmentation process and the Geographic Object Based Image Analysis (GEOBIA) framework. eCognition software with I-Os as classification unit and maximum likelihood algorithm facilitated the process. Supervised classification approaches (SCA) and rule set classification approach (RSCA) were used and performance and transferability of two approaches compared. Main conclusions: (a) high degree of details in GeoEYE data enables delineation of diverse land use zones, and (b) segmentation based analysis is more effective to tackle spatial intermixing.

Indicadores de síntomas ADHD en el contexto de aprendizaje virtual, utilizando técnicas de aprendizaje automático

Rev.esc.adm.negEste artículo presenta un proceso de modelado de usuario, específicamente un modelado de estudiante, en un ambiente virtual de aprendizaje, que permite inferir la presencia o no de síntomas del Déficit de Atención e Hiperactividad (TDAH). El modelo de usuario es construido teniendo en cuenta tres características del estudiante: Conducta de comportamiento (BC), Rendimiento de funciones ejecutivas (EFP), y estado emocional (ES). Para inferir si un estudiante puede tener un perfil asintomático de TDAH, se usa un grupo de reglas de clasificación que usan los resultados obtenidos en cada característica como datos de entrada para su funcionamiento. Basados en las pruebas del modelo propuesto, se obtiene un grupo de entrenamiento que es usado para preparar un algoritmo de aprendizaje automático, el cual podrá realizar y mejorar la tarea de crear el perfil para cada estudiante de acuerdo a si presenta o no síntomas del TDAH o problemas de atención. Esto, puede ser el primer paso para ofrecer recursos de aprendizajesadaptados a las necesidades educativas de estudiantes que presenten este trastorno.

UNCERTAINTY HANDLING IN DISASTER MANAGEMENT USING HIERARCHICAL ROUGH SET GRANULATION

Abstract. Uncertainty is one of the main concerns in geospatial data analysis. It affects different parts of decision making based on such data. In this paper, a new methodology to handle uncertainty for multi-criteria decision making problems is proposed. It integrates hierarchical rough granulation and rule extraction to build an accurate classifier. Rough granulation provides information granules with a detailed quality assessment. The granules are the basis for the rule extraction in granular computing, which applies quality measures on the rules to obtain the best set of classification rules. The proposed methodology is applied to assess seismic physical vulnerability in Tehran. Six effective criteria reflecting building age, height and material, topographic slope and earthquake intensity of the North Tehran fault have been tested. The criteria were discretized and the data set was granulated using a hierarchical rough method, where the best describing granules are determined according to the quality measures. The granules are fed into the granular computing algorithm resulting in classification rules that provide the highest prediction quality. This detailed uncertainty management resulted in 84% accuracy in prediction in a training data set. It was applied next to the whole study area to obtain the seismic vulnerability map of Tehran. A sensitivity analysis proved that earthquake intensity is the most effective criterion in the seismic vulnerability assessment of Tehran.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system — the southern power system of Hebei province.

Mapping Orchards on Plain Terrains Using Multi-Temporal Medium-Resolution Satellite Imagery

<abstract> <b><sc>Abstract. </sc></b>The ability to map the spatiotemporal distribution of orchards is essential in order to optimally utilize and manage their available resources. By analyzing multi-temporal characteristic and spectral features of medium-resolution images, the optimum features required to extract orchard data are determined and a classification rule set based on the object-based information analysis method (OBIA) is built. For those years in which images at key time points were missing, the extracted orchard data were used in the extraction process for these years as a thematic layer. The method was tested in Dangshan County, located in central China. Time series maps derived from the eight years of data over a nearly 20-year period from the Landsat Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-HJ-1 satellite Charge Coupled Device agree well with field samples and historical records. Among the derived maps, the accuracy of orchard data derived from the 2010 HJ-1 images was estimated at about 92.9%. Compared with the common index, NDVI, the inclusion of IOPT (index for orchard in plain terrain) in the extraction process can lead to a significant improvement. When using a single-date image, the accuracies were all less than 70.0%, and when using multi-temporal images, an accuracy of 85.7% was achieved.

Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach

Prominence of data streams has dragged the interest of many researchers in the recent past. Mining associative rules generated on data streams for prediction has raised greater research interest in recent years. Associative classification mining has shown better performance over many former classification techniques in Data Mining and Data Stream Mining domains. This paper introduces a new technique for mining data streams using associative classification. To the best of our knowledge there are only few techniques existing. We designed a compact data structure to efficiently maintain data streams without losing any important information. We present a PSToSW for mining rules from the tree. Subsequently, an optimized algorithm called PSToSWMine is proposed for mining a classifier which contains set of high qualified classification rules. We then conduct experiments using synthetic and real data sets to assess the performance of our approach. The experimental results show that our technique is superior to existing algorithms which perform similar tasks in terms of accuracy of prediction and run time efficiency. General Terms Data Stream Classification, Algorithms et. al.

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

A Novel Methodology for Constructing Rule-Based Naïve Bayesian Classifiers

Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naive Bayesian classification. Naive Bayesian classification is a probabilistic classification approach that uses Bayesian Theorem to predict the classes of unclassified records. A drawback of Naive Bayesian Classification is that every time a new data record is to be classified, the entire dataset needs to be scanned in order to apply a set of equations that perform the classification. Scanning the dataset is normally a very costly step especially if the dataset is very large. To alleviate this problem, a new approach for using naive Bayesian classification is introduced in this study. In this approach, a set of classification rules is constructed on top of naive Bayesian classifier. Hence we call this approach Rule-based Naive Bayesian Classifier (RNBC). In RNBC, the dataset is canned only once, off-line, at the time of building the classification rule set. Subsequent scanning of the dataset, is avoided. Furthermore, this study introduces a simple three-step methodology for constructing the classification rule set.

A data-mining-based methodology to support MV electricity customers’ characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supported by knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MV consumers and to develop a rule set for the automatic classification of new consumers. To achieve our goal a methodology is proposed consisting of several steps: data pre-processing; application of several clustering algorithms to segment the daily load profiles; selection of the best partition, corresponding to the best consumers’ segmentation, based on the assessments of several clustering validity indices; and finally, a classification model is built based on the resulting clusters. To validate the proposed framework, a case study which includes a real database of MV consumers is performed.