Two-Stage Constructing Hyper-Plane for Each Test Node of Decision Tree

2010 ◽  
Vol 26-28 ◽  
pp. 776-779
Author(s):  
Wei She ◽  
Hong Li ◽  
Guo Qing Yu ◽  
Rui Deng
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Two Stage ◽  
Combination Methods ◽  
Hyper Plane ◽  
Two Stages ◽  
The Impact ◽  
Better Than

How to construct the “appropriate” split hyper-plane in test nodes is the key of building decision trees. Unlike a univariate decision tree, a multivariate (oblique) decision tree could find the hyper-plane that is not orthogonal to the features’ axes. In this paper, we re-explain the process of building test nodes in terms of geometry. Based on this, we propose a method of learning the hyper-plane with two stages. The tree (TSDT) induced in this way keeps the interpretability of univariate decision trees and the trait of multivariate decision trees which could find oblique hyper-plane. The tests of the impact of Combination methods tell us that TSDT based combination algorithm is much better than other tree based combination methods in accuracy.

Effect of female executives on Chinese cultural media enterprise efficiency

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ying Li ◽  
Yung-Ho Chiu ◽  
Tai-Yu Lin ◽  
Hongyi Cen
Keyword(s):  
Boards Of Directors ◽  
Company Performance ◽  
Tobit Regression ◽  
Female Executives ◽  
Two Stage ◽  
Content Type ◽  
Female Directors ◽  
The Impact ◽  
Cultural Media ◽  
Better Than

Purpose As more women are now being appointed to senior and top management positions and invited to sit on boards of directors, they are now directly participating in strategic company decision-making. As female directors have been found to provide new ideas, increase company competitiveness, efficiency and performance and bring a greater number of external resources to a company than male directors, this paper aims to put female directors as a variable into the data envelopment analysis (DEA) and statistical models to explore the effect of female directors on operating performances. The DEA first quantified and measured the company efficiencies, after which the statistical model analyzed the correlations between the variables to specifically identify the impact of female decision makers on the operating efficiencies in state-owned and private enterprises. Design/methodology/approach A novel two-stage, meta-hybrid dynamic DEA was developed to explore Chinese cultural media company efficiencies under optimal input and output resource allocations, after which Tobit Regression was applied to determine the effect of female executives on these efficiencies. Findings From 2012 to 2016, the overall efficiencies in Chinese state-owned cultural media enterprises were better than in the private cultural media enterprises. The overall technology gaps (TGs) in the state-owned cultural media enterprises were better than in the private cultural media enterprises. Originality/value Previous research has tended to focus on the causal relationships between female senior executives and business performances; however, there have been few studies on the relationships between female executives and company performance from an efficiency perspective (optimal resource allocation). This paper, therefore, is the first to develop a novel two-stage, meta-hybrid dynamic DEA to examine Chinese cultural media enterprise efficiencies, and the first to apply Tobit Regression to assess the effect of female executives on those efficiencies.

Global Induction of Decision Trees

2011 ◽  
pp. 937-942
Author(s):  
Marek Kretowski ◽  
Marek Grzes
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Decision Rules ◽  
Knowledge Discovery In Databases ◽  
Binary Outcomes ◽  
Multivariate Tests ◽  
Axis Parallel ◽  
Hyper Plane ◽  
Single Attribute ◽  
Multivariate Systems

Decision trees are, besides decision rules, one of the most popular forms of knowledge representation in Knowledge Discovery in Databases process (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996) and implementations of the classical decision tree induction algorithms are included in the majority of data mining systems. A hierarchical structure of a tree-based classifier, where appropriate tests from consecutive nodes are subsequently applied, closely resembles a human way of decision making. This makes decision trees natural and easy to understand even for an inexperienced analyst. The popularity of the decision tree approach can also be explained by their ease of application, fast classification and what may be the most important, their effectiveness. Two main types of decision trees can be distinguished by the type of tests in non-terminal nodes: univariate and multivariate decision trees. In the first group, a single attribute is used in each test. For a continuousvalued feature usually an inequality test with binary outcomes is applied and for a nominal attribute mutually exclusive groups of attribute values are associated with outcomes. As a good representative of univariate inducers, the well-known C4.5 system developed by Quinlan (1993) should be mentioned. In univariate trees a split is equivalent to partitioning the feature space with an axis-parallel hyper-plane. If decision boundaries of a particular dataset are not axis-parallel, using such tests may lead to an overcomplicated classifier. This situation is known as the “staircase effect”. The problem can be mitigated by applying more sophisticated multivariate tests, where more than one feature can be taken into account. The most common form of such tests is an oblique split, which is based on a linear combination of features (hyper-plane). The decision tree which applies only oblique tests is often called oblique or linear, whereas heterogeneous trees with univariate, linear and other multivariate (e.g., instance-based) tests can be called mixed decision trees (Llora & Wilson, 2004). It should be emphasized that computational complexity of the multivariate induction is generally significantly higher than the univariate induction. CART (Breiman, Friedman, Olshen & Stone, 1984) and OC1 (Murthy, Kasif & Salzberg, 1994) are well known examples of multivariate systems.

An Algorithm to Optimal Two-Stage Homogenous Strip Patterns of Rectangular Pieces

2011 ◽  
Vol 347-353 ◽  
pp. 3189-3192
Author(s):  
Jun Ji ◽  
Yi Ping Lu ◽  
Jian Zhong Cha ◽  
Yao Dong Cui ◽  
Ling Jun Kong
Keyword(s):  
Dynamic Programming ◽  
Knapsack Problems ◽  
Benchmark Problems ◽  
Two Stage ◽  
Strip Layout ◽  
Punching Process ◽  
Two Stages ◽  
Computation Speed ◽  
Better Than

This paper presents an algorithm for two-stage homogenous strip patterns for rectangular pieces. The algorithm is appropriate for the shearing and punching process. It proposes the two-stage homogenous strip patterns that can be cut into homogenous strips in two stages, with another two stages being required to cut the strips into pieces. Firstly vertical cuts divide the stock sheet into segments, and then horizontal cuts divide the segments into homogenous strips. A homogenous strip contains pieces of the same type. The algorithm uses a dynamic programming recursion to determine the strip layout on each segment, solves knapsack problems to obtain the segment layout on the sheet. The algorithm is tested through benchmark problems, and compares with two famous algorithms. The pattern value and the computation speed of this paper’s algorithm are better than that of the classic two-stage algorithm. What’s more, this paper’s algorithm can give solutions very close to optimality.

scholarly journals C4.5 Algorithm To Predict The Impact Of The Earthquake

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Efori Buulolo ◽  
Natalia Silalahi ◽  
Fadlina
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Tree Form ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
The Impact

One of the impacts of the quake was heavily damaged, the even tsunami killed at no less. One cause many deaths is because many can not predict the impact of earthquakes. Data earthquakes that occurred earlier can be used to predict the incidence of the quake will probably happen someday. One algorithm that can be used to predict is the algorithm C4.5. The results of the algorithm C4.5 decision tree form, decision trees characteristic or condition of the earthquake and the decision, where the decision is a fruit of the earthquake that occurred modeling

scholarly journals Using online decision trees to support students’ self-efficacy in the laboratory

2020 ◽  
Vol 44 (3) ◽  
pp. 430-435
Author(s):  
Sarah McLean ◽  
Ken N. Meadows ◽  
Austin Heffernan ◽  
Nicole Campbell
Keyword(s):  
Problem Solving ◽  
Decision Tree ◽  
Decision Trees ◽  
Self Efficacy ◽  
Extrinsic Motivation ◽  
Problem Solving Skills ◽  
Safe Place ◽  
Science Laboratories ◽  
Undergraduate Science ◽  
The Impact

Failed experiments are a common occurrence in research, yet many undergraduate science laboratories rely on established protocols to ensure students are able to obtain results. While it is logistically challenging to facilitate students’ conducting their own experiments in the laboratory, allowing students to “fail” in a safe environment could help with the development of problem-solving skills. To allow students a safe place to fail and encourage them to think through a laboratory protocol, online decision trees were created to lead students through protocols and give them timely feedback. The online decision trees present students with a scenario, then students execute a protocol by selecting options that will lead them down different paths and result in various realistic results from their experiments. They receive feedback and instructional tutorials throughout the simulation that are dependent on their choices. The significance of this new resource for student learning is that it allows students to practice their problem-solving skills and gain theoretical knowledge about the purpose of various experimental steps. The purpose of this research study was to evaluate whether online decision trees affected students’ self-efficacy, metacognition, and motivation for completing a wet laboratory. A mixed-methods approach was used; three surveys were administered throughout the academic term. For survey 1, students completed the decision tree and survey before the wet laboratory. For survey 2, students completed the survey before the wet laboratory but completed the decision tree after the wet laboratory. Students’ reported self-efficacy and intrinsic motivation were increased with the administration of the online decision trees before the wet laboratory, but their extrinsic motivation and metacognitive scores were unchanged. For survey 3, students provided written feedback about the impact of the online decision trees, and their responses highlighted the importance of the visual components of the approach.

Prolonged survival after two-stage resection of advanced colorectal liver metastases: Impact of an intensified chemotherapy.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 748-748
Author(s):  
Marie-Hélène Pissas ◽  
Sebastien Carrere ◽  
Lise Roca ◽  
Pierre-Emmanuel Colombo ◽  
Martin Bertrand ◽  
...  
Keyword(s):  
Overall Survival ◽  
Liver Metastases ◽  
Colorectal Liver Metastases ◽  
Median Overall Survival ◽  
Two Stage ◽  
Excellent Outcome ◽  
Second Resection ◽  
Intensified Chemotherapy ◽  
Two Stages ◽  
The Impact

748 Background: Patients with advanced colorectal liver metastases (CRLM) experience poor prognosis. The impact of two-stage resection (TSR) after downstaging by chemotherapy is still controversial. Methods: Data on 899 patients with CRLM in a single institution during a 9-year period (2004–2013) were prospectively collected. We used intent-to-treat analysis to evaluate the survival of patients who underwent TSR associated with intensified chemotherapy before and between the two surgical stages. Results: 73 patients were eligible for the first stage of TSR. In this population, 54 patients underwent an intensified chemotherapy based on FOLFIRINOX (26 patients) or a standard chemotherapy associated with cetuximab or bevacizumab (28 patients). The first surgical stage was a clearance of the left liver in 56% of cases. An average of two radio-frequency ablations and two wedge resections were necessary. The post-operative morbidity of the first stage was 18%. 78% of patients received chemotherapy between the two stages. The average interval between two stages was 228 days (36-1561). 68% of TSR patients completed the second stage. The second resection was mainly a standard right lobectomy (32%). Morbidity after the second resection was 12%. One patient died post-operatively because of post operative liver failure. Median overall survival of patients who completed TSR was 48 months. In contrast, there was no survival advantage for patients who underwent only the first stage because of progression (median overall survival: 19 months) (p = 0.0003). The median overall survival of the whole population was 43 months and the median recurrence-free survival was 15 months. Conclusions: Intensified chemotherapy in association with TSR allows excellent outcome in patients with advanced CRLM. Chemotherapy delivered between the two surgical stages is responsible for an important waiting time but could contribute to a better control of the evolution of the disease.

Applications of Data Mining Algorithm in Equipment Fault Diagnosis

2014 ◽  
Vol 644-650 ◽  
pp. 2551-2555
Author(s):  
Rong Xiang Li ◽  
Zeng Lei Zhang ◽  
Yun Liu ◽  
Shan Chao Tu
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Decision Trees ◽  
Data Mining Algorithm ◽  
Basic Principles ◽  
Mining Algorithm ◽  
Id3 Algorithm ◽  
Better Than ◽  
Improved Algorithm

The Basic Principles of Data mining Decision-tree ID3 is opened out. The main deficiencies are analysed. An improved algorithm based on the ID3 is calculated. For fault diagnosis of engine exemple, traditional ID3 algorithm and the improved algorithm are applied to estimate the fault diagnosis of engine separately. Decision Trees of traditional ID3 algorithm and the improved algorithm are construct. Experiment result display the accuracy of improved algorithm is better than traditional ID3. The improved algorithm is more fit to applied to the equipment fault diagnosis.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

scholarly journals Advertising Media Impact in Consumer Buying Behavior

2019 ◽  
Vol 8 ◽  
pp. 54-56
Author(s):  
Ashmita Dahal Chhetri
Keyword(s):  
Consumer Behavior ◽  
Young Male ◽  
Electronic Media ◽  
Buying Behavior ◽  
Media Impact ◽  
High Degree ◽  
The Impact ◽  
Buying Behaviors ◽  
The Relationship ◽  
Better Than

Advertisements have been used for many years to influence the buying behaviors of the consumers. Advertisements are helpful in creating the awareness and perception among the customers of a product. This particular research was conducted on the 100 young male and female who use different brands of product to check the influence of advertisement on their buying behavior while creating the awareness and building the perceptions. Correlation, regression and other statistical tools were used to identify the relationship between these variables. The results revealed that the relationship between media and consumer behavior is positive. The adve1tising impact on sales and there is positive and high degree relationship between advertising and consumer behavior. The impact on advertising of a product of electronic media is better than non-electronic media.

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