Decision Tree Classification Technique Research Articles

Spare part supplier selection model using decision tree classification techniques: J48 Algorithm

Spare parts are goods that consist of several components that form a single unit and have certain functions. To facilitate companies in selecting suppliers, a supplier selection model is needed to make it easier for companies to select suppliers and make it easier to see strategic directions to take several criteria from suppliers to achieve priorities. This research was conducted at a manufacturing company engaged in car spare parts with rubber raw materials. In this study the problems that occur are the difficulty of the company in selecting suppliers based on criteria that are in accordance with the company and the delay in the receipt of raw materials, and the lack of raw materials supplied from each supplier. This study aims to classify suppliers based on desired criteria by the company and to design a supplier selection model in the long term. The J48 algorithm is produced by a supplier selection model with 2 rule selection models to classify efficient suppliers and inefficient suppliers. The accuracy of the Decision Tree model is 90.8547%, the MAE error value is 0.1256. From the J48 algorithm the biggest gain is the criteria for quality, price, delivery, and warranty and complaint services.

Detecting Spatiotemporal Expansion of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Northern Ethiopia

In the Northern Ethiopia, Lake Tana has serious problems related to the alien water hyacinth invasive weed. The weed species grows in and swims over water and expands very quickly. The purpose of this study is to detect the spatiotemporal trend of the weed during 2013, 2015 and 2017. Landsat 8 satellite images captured in December were used to assess the rate of expansion of the water hyacinth. Training samples were collected for supervised classification of the satellite images and accuracy assessment. Image preprocessing and image enhancement was carried out before classification. The expansion and distribution of the water hyacinth was analyzed using decision tree and maximum likelihood classification techniques. The classification accuracy assessment result for 2017 reported 99.5% overall accuracy and 98% of kappa coefficient. The result depicted that in 2013, water hyacinth coverage was very small in its coverage and was estimated to be 112.1 ha (0.17%) of the study area. In 2015, the coverage of the weed showed little significant change as of 2013 and grew up to 168.7 ha (0.25%). However, after two years, its aerial coverage was dynamically expanded to 1512 ha (2.25%) of the study area in 2017. The result indicated that there was rapid expansion of the weed species from 2015 to 2017s. The coverage increased by 50% in the period between 2013 and 2015 and by 82% in the period between 2015 and 2017. Since the expansion of the weed is extremely fast and endangers the existence of the water, appropriate intervention mechanisms should be urgently introduced.

Classification of the temperature-dependent gain of an erbium-doped fiber amplifier by using data mining methods

In this study, the experimental results of an erbium-doped fiber amplifier (EDFA) system designed and experimentally installed in the C-band were classified by using data mining methods. The non-linearity of the EDFA gain with temperature is a problem for EDFA designers. For this reason, the wavelength range to be used in the design, the temperature values and change in the input power values of signals coming to the design are not linear with respect to the output power. Data mining method based on different models are proposed to solve this problem. The data set used contains four variables: temperature, wavelength, input power, and output power. The success results of at WEKA software (Waikato environment for knowledge analysis) were compared with 6 different algorithms. As a result of the analysis made, the C4.5 algorithm, which is the decision tree classification technique, obtained 98.4 % close results in experimental results. When the results are evaluated, data mining algorithms can be constructed and validated using larger data sets. This will help the fiber optic data to be quickly understood and easy to design for optical designers in practical applications.

Intrusion Detection System using Datamining Based Enhanced Framework

With the significant increase in the use of computers over the network and the development of applications on different platforms, the focus is on network security. The identification of multiple attacks is actually an important element of network security. The role of the IDS is to track and prevent unauthorized use or damage to network resources and systems. An intrusion detection system using Datamining Based Enhanced Framework (DEF) is presented in this paper. The model is assisted by the K-mean Clustering and Decision Tree (DT) classification techniques in which genetic algorithms (GA) for clusters, max runs and confidence can be used. The experimental results shows the promising outcome of the proposed Datamining Based Enhanced Framework (DEF).

Changes of Wiang Nong Lom and Nong Luang Wetlands in Chiang Saen Valley (Chiang Rai Province, Thailand) During the Period 1988–2017

Pressure on the Wiang Nong Lom and Nong Luang wetland resources in the Chiang Saen Valley of Chiang Rai Province (Thailand) has increased in recent years with the expansion of farmlands and other major sources of wetland conflict related to public land encroachment. Both of these wetlands have been designated as strategic ecosystems. Yet, there is a limited understanding of the way different wetlands respond to change drivers (agriculture, climate, population, etc.), and currently no scientifically valid protocols exist for local wetland mapping and monitoring. Distinguishing between small wetlands and land use and land cover (LULC) components is a challenging affair due to the highly heterogeneous landscape and spectral similarity of compositionally different types of tropical regions. The goals of this article are both technological and substantive, i.e., it aims to (A) propose a synthesis of quantitative techniques that can improve LULC mapping using remotely sensed data (Landsat TM, ETM+, OLI), and (B) assess the wetland changes during the last three decades and better understand the interaction between wetland changes, human population, and the environment. In regards to goal (A), the proposed classification approach employed a synthesis of techniques of decision tree classification (DTC), maximum likelihood classification (MLC), and Mahalanobis distance classification (MDC), with different bands and ancillary data inputs. The results demonstrated that the implementation of the DTC algorithms to address LULC mapping problems exhibited an overall mapping accuracy of 83.9%, which is significantly higher than that of MLC and MDC. It was found that the DTC technique performs well when combined with visible, near-infrared (NIR), and shortwave-infrared bands, a digital elevation model and normalized difference vegetation index layers. Subsequently, the postclassification analysis using DTC showed a notable improvement of approximately 88.0% classification accuracy. Regarding goal (B), our results showed that during the last 30 years, wetland areas in the Chiang Saen Valley have experienced a dramatic decrease of 30.5%, whereas forest landscape surrounding the wetlands has decreased by an astonishing 50.9%. Contrarily, we found that agricultural land size has increased by 24.3%. We suggest that ground data can be linked to the etiology of these results, including the gradual conversion of wetlands to rice cultivation fields as a result of the government rice pledging scheme. Large areas in the study region have been cultivated by para-rubber, palm oil, and pineapple agribusiness production since 2003. In addition, short-term subsidizing government policies promote intensive production for commercial agriculture prompting farmers to transition from subsistence to commercial farming, further impacting wetland conversion. As a result, and in further view of the fact that rapidly expanding agricultural areas have contributed significantly to the decrease of wetland areas during the last three decades, the Chiang Saen Valley wetlands have been designated as wetlands of international importance. The overall recommendation of the present work is that special land-use policy and relevant regulation and/or legislation are critical components of any effort to achieve wetland sustainability.

Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques

Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques

Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark

Soil drainage constitutes a substantial factor affecting plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Consequently, soil drainage maps represent crucial tools for crop, forest and environmental management purposes. As extensive field surveys are time- and resource-consuming, alternative spatial modelling techniques have been previously applied for predicting soil drainage classes. The present study assessed the use of Artificial Neural Networks (ANN) for mapping soil drainage classes in Denmark and compared it to a Decision Tree Classification (DTC) technique. 1702 soil observations and 31 environmental variables, including soil and terrain parameters, and spectral indices derived from satellite images, were utilized as input data. Based on a 33% holdback validation dataset, the best performing ANN and DTC models yielded overall accuracy values of 54 and 52%, respectively. DTC models benefited from the use of all variables, but ANN models performed better after variable selection. Notably, ANN and DTC model performances were comparable although differential costs for misclassification were only implemented for DTC modelling. Nevertheless, both methods produced predictive drainage maps in accordance with one another and demonstrated promising classification abilities over a large study area (c. 43,000km2).

Use of the Decision Tree Technique to Estimate Sugarcane Productivity Under Edaphoclimatic Conditions

A number of biometric evaluations are performed during harvest for measuring the growth and development of the sugarcane crop. From these evaluations, hundreds of data values are generated, containing certain information on the productivity of the culture in that crop and edaphoclimatic region. Accordingly, the objective of this work was to identify, using a decision tree classification technique, the biometric attribute having the greatest effect on the productivity of the plant cane in different planting configurations and edaphoclimatic conditions. To accomplish this, data were evaluated from four experiments with sugarcane, located within the Sao Paulo municipalities of Teodoro Sampaio, Guaira, Iracemapolis, and Lencois Paulista. The classification model was generated using the decision tree technique, a type of intuitive learning that creates a hypothesis based on particular instances that results in general conclusions. The decision trees applied to the data of the four sites showed that the population of plants per hectare has the highest information gain (split attribute) on the class attribute (productivity). Using the “Chi-square” method of attribute selection, the population of plants per hectare was observed to have the largest correlation with the final productivity of the culture. Therefore, the decision tree indicates that the attribute “plant population per area” should be used as the method to evaluate the productive potential of the culture during its growth cycle. It has the best correlation with the final productivity of the crop, in addition to being an attribute easy to measure in the field.

Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.

Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques

Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.). To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA). Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.

A Data Mining Approach to Predict Prospective Business Sectors for Lending in Retail Banking Using Decision Tree

A potential objective of every financial organization is to retain existing customers and attain new prospective customers for long-term. The economic behaviour of customer and the nature of the organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking. Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk; and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are many existing systems on customer retention as well as customer attrition systems in bank, these rigorous methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used records of business customers of a retail commercial bank in the city including rural and urban area of (Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for analysing the challenging issues, where pruned decision tree classification technique has been used to develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to build up a model to predict prospective business sectors in retail banking.

Geomorphology Analysis of Lava Flow of Mt. Guntur in West Java Using Synthetic Aperture Radar (SAR) with Fully Polarimetry

Mt. Guntur is one of active volcanoes in Indonesia, which has a very unique product of lava flows than other volcanoes. In order to minimize the adverse effects of volcanic eruptions of Mt. Guntur, one mitigation measure that can be done is to map the volcano landform through geomorphological analysis and identification of lava flow characteristics. It is very obvious that Mt. Guntur lava flow is visible and clear so it is possible to do the landform lava flow mapping. However, this kind of study is very rare especially in Indonesia. Thus, this research aims to analyse the geomorphologic and identify lava flows using high-resolution optical imagery through characterization (signature) landforms by using L band SAR polarimetry backscattering in combination with decision tree classification techniques using the QUEST algorithm (Quick, Unbiased, Efficient Statistical Trees). Moreover, the accuracy of the classification results is calculated using matrix analysis accuracy and Kappa coefficient calculation. In addition, the characterization of the object is also performed using analysis of spectral separation. The analysis showed that the geomorphological analysis can be used for volcanic (V) landforms mapping based on morfocronologi aspect. The identification of the image depends on the spatial resolution of the image. The objects were identified using IKONOS imagery, Google Earth, and PALSAR imagery. The results of showed that the HV and VV polarization serves as the best combination to identify the lava flows. Classification results showed a good accuracy value which is 51.80% with a Kappa coefficient of 0.43. This suggests that the identification of a lava flow using linear polarization combined with decision tree classification has a good level of confidence.

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

To develop an effective nondestructive evaluation method for the welding quality of the resistance spot welding, the electrode displacement signal during the resistance spot welding process is monitored, and the acquisition data of the signal are innovatively presented as the radar chart format. Some geometric features of the radar charts are extracted to reflect the welding quality. The decision tree classification technique is adopted to build a classifier and to provide a visible and intuitive diagnostic procedure for welding quality assessment. Test results of the decision tree classifier show that it is feasible and reliable to evaluate weld quality based on the graphics features of the radar chart. The features and the weld quality are closely related, and their extraction avoids complex algorithm. Meanwhile, when there are small samples, the decision tree classifier can identify good or bad weld accurately and rapidly, even though the weld is from an abnormal welding process, such as expulsion, current shunting, greasy surface, and small edge distance condition.

Research on Duplicated Documentation Removal Model Based on Information Entropy and Decision Classification Techniques

Electronic document is presented in the form of data table through applying characterization and discrete processing method to its type, size, MD5 value etc. A duplication removal model for documentation is constructed by using information entropy based decision tree classification technique. Simple experiments are carried out that show that the proposed model is feasible to a certain degree and can achieve documentation’s duplication removal to a certain extent.

Oblique Decision Tree Learning Approaches - A Critical Review

Decision tree classification techniques are currently gaining increasing impact especially in the light of the ongoing growth of data mining services. A central challenge for the decision tree classification is the identification of split rule and correct attributes. In this context, the article aims at presenting the current state of research on different techniques for classification using oblique decision tree. A variation to the traditional approach is the called oblique decision tree or multivariate decision tree, which allows multivariate tests in its non-terminal nodes. Univariate trees can only perform axis-parallel splits, whereas Oblique decision trees can model the decision boundaries that are oblique to attribute axis. The majority of these decision tree induction algorithms performs a top-down growing tree strategy and relay on an impuritybased measure for splitting nodes criteria. In this context, the article aims at presenting the current state of research on different techniques for Oblique Decision Tree classification. For this, the paper analyzes various traditional Multivariate and Oblique Decision Tree algorithms CART, OC1 as well as standard SVM, GDT implementation. General Terms .Data mining ,algorithm for tree building.

Monitoring of Rapid Urban Sprawl in Beijing with Time Series Remote Sensing Data and Analysis of Driving Forces

Beijing has experienced a rapid urban sprawl over the past three decades, along with accelerated socio-economic development. This study investigated the change patterns and figured out the driving forces of urban expansion in the study area. To obtain urban class, decision tree classification techniques were used to identify the land cover types using four scenes of Landsat images from four periods of 1978-era, 1992-era, 2000-era and 2010-era. Then, the urban areas were identified by excluding water, agriculture, forest, grassland and bare land. The analysis results showed that: 1) urban construction land had been expanded very quickly and the urban area is mainly in the south-central part of the municipality; 2) the urban area increased by 96284.97 ha and the ratio was 5.88%; and 3) population growth, economic development, urban construction and industrial structure adjustment could explain the expansion. These analysis results can provide significant information on the monitoring and management of sustainable urban development.

Analysing spatio-temporal footprints of urbanization on environment of Surat city using satellite-derived bio-physical parameters

Our study examines the relationships among various environmental variables in Surat city using remote sensing. Landsat Thematic Mapper satellite data were used in conjugation with geospatial techniques to study urbanization and correlation among satellite-derived biophysical parameters namely, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference bareness index (NDBaI) and land surface temperature (LST). A modified NDWI (MNDWI) was used for extracting areas under water. Land use/land cover classification was performed using hierarchical decision tree classification technique using ERDAS IMAGINE Expert classifier with an accuracy of 90.4% for 1990 and 85% for 2009. It was found that city has expanded over 42.75 sq.km within two decades. Built-up, fallow and sediment land use classes exhibited high dynamics with increase of nearly 200% and 50% and decrease of 55% respectively from 1990 to 2009. Vegetation and water classes were less dynamic with 20% decrease and 15% increase. The transformation of land parcels from vegetation to built-up, vegetation to fallow and fallow to built-up has resulted in increase of LST by 5.5 ± 2.6°C, 6.7 ± 3°C and 3.5 ± 2.9°C, respectively.

Conjecturable knowledge discovery: A fuzzy clustering approach

Traditionally, clustering is the task of dividing objects into homogeneous clusters based on their degrees of similarity. As objects are assigned to clusters, users need to manually give descriptions for all clusters. Characterizing clusters by hand can consume a great deal of time of users. In addition, users sometimes have no specific idea as to how to explain the clustering results; thus, they might give inappropriate descriptions. A clustering technique is proposed to discover conjecturable rules, providing descriptions of clusters with a decision tree classification technique. Every cluster in a conjecturable tree is depicted by only one conjecturable rule. However, less-utilized rules are not necessarily trivial. In some real-life circumstances, there might be some clusters which can be depicted by two or more rules, namely, recessive conjecturable rules. For example, customers usually prefer to buy inexpensive red wines; however, on certain occasions, such for a birthday celebration, they will buy expensive wine. Therefore, we know that there are some people who generally belong to a low-value cluster but may simultaneously be assigned to a high-value one. In this study, we propose a new discovery model for mining conjecturable rules to reveal this type of knowledge. The experimental results show that our proposed model is able to discover conjecturable rules as well as recessive rules. The results of sensitivity analysis are also given for practitioners' reference.

Impacts of land use and land cover changes on evapotranspiration and runoff at Shalamulun River watershed, China

The study assesses the effect of land use and land cover changes (LUCC) on evapotranspiration and runoff in the Shalamulun River watershed of 2,453 km2 located in Inner Mongolia Autonomic Region of China. First, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1987, 2001 and 2007 are used to quantify LUCC in the watershed. A knowledge-based decision tree (K-DT) classification technique is used to detect LUCC. By comparison of post-classification change among 1987, 2001 and 2007, the results showed significant modification and conversion of land use and cover of the watershed over the 20-year period 1987–2007. The results show that the forest area underwent the greatest change, decreasing by 159.2 km2 in the study period. At the same time, the area of farmland, barren land and residential land increased by 89.5, 46.4 and 25.3 km2, respectively. Subsequently, a two-source potential evapotranspiration (PET) model is used to estimate the potential evapotranspiration response to LUCC. Finally, the influence of LUCC on annual runoff is evaluated using a statistical method. LUCC potentially caused a decrease in annual PET and runoff. Meanwhile, the land use changes resulted in spatio-temporal variations of monthly PET in the growing season (April–September).