COMPARISON OF VARIOUS ROUTINES FOR UNKNOWN ATTRIBUTE VALUE PROCESSING: THE COVERING PARADIGM

1996 ◽  
Vol 10 (08) ◽  
pp. 939-955 ◽  
Author(s):  
IVAN BRUHA ◽  
FRANTISEK FRANEK
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Decision Rules ◽  
Medical Data ◽  
Definition Of ◽  
Attribute Value

Simple inductive learning algorithms assume that all attribute values are available. The well-known Quinlan's paper1 discusses quite a few routines for the processing of unknown attribute values in the TDIDT family and analyzes seven of them. This paper introduces five routines for the processing of unknown attribute values that have been designed for the CN4 learning algorithm, a large extension of the well-known CN2. Both algorithms CN2 and CN4 induce lists of decision rules from examples applying the covering paradigm. CN2 offers two ways for the processing of unknown attribute values. The CN4's five routines differ in style of matching complexes with examples (objects) that involve unknown attribute values. The definition of matching is discussed in detail in the paper. The strategy of unknown value processing is described both for learning and classification phases in individual routines. The results of experiments with various percentages of unknown attribute values on real-world (mostly medical) data are presented and performances of all five routines are compared.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm

2018 ◽  
Vol 26 (1) ◽  
pp. 43-66 ◽  
Author(s):  
Uday Kamath ◽  
Carlotta Domeniconi ◽  
Kenneth De Jong
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Real World Data ◽  
Meta Level ◽  
Meta Learning ◽  
Robustness To Noise ◽  
Boosting Algorithm ◽  
Efficient Learning ◽  
Empirical Analyses

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

scholarly journals How to Shift Bias: Lessons from the Baldwin Effect

1996 ◽  
Vol 4 (3) ◽  
pp. 271-295 ◽  
Author(s):  
Peter Turney
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Darwinian Evolution ◽  
Baldwin Effect ◽  
Lamarckian Evolution ◽  
Inheritance Of Acquired Characteristics ◽  
The Baldwin Effect ◽  
Fixed Bias ◽  
Shift Of Bias

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896 to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.

scholarly journals RULES-F: A fuzzy inductive learning algorithm

2006 ◽  
Vol 220 (9) ◽  
pp. 1433-1447 ◽  
Author(s):  
D T Pham ◽  
S Bigot ◽  
S S Dimov
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Control Applications ◽  
Fuzzy Models ◽  
And Performance

Current inductive learning algorithms have difficulties handling attributes with numerical values. This paper presents RULES-F, a new fuzzy inductive learning algorithm in the RULES family, which integrates the capabilities and performance of a good inductive learning algorithm for classification applications with the ability to create accurate and compact fuzzy models for the generation of numerical outputs. The performance of RULES-F in two simulated control applications involving numerical output parameters is demonstrated and compared with that of the well-known fuzzy rule induction algorithm by Wang and Mendel.

Cost-Sensitive Action Model Learning

2016 ◽  
Vol 24 (02) ◽  
pp. 167-193 ◽  
Author(s):  
Dongning Rao ◽  
Zhihua Jiang
Keyword(s):  
Online Learning ◽  
Real World ◽  
Learning Algorithm ◽  
High Rate ◽  
Model Learning ◽  
Total Cost ◽  
Cost Constraints ◽  
Action Model ◽  
Definition Of ◽  
The Cost

Action model learning can relieve people from writing planning domain descriptions from scratch. Real-world learners need to be sensitive to all kinds of expenses which it will spend in the learning. However, most of previous studies in this research line only considered the running time as the learning cost. In real-world applications, we will spend extra expense when we carry out actions or get observations, particularly for online learning. The learning algorithm should apply more techniques for saving the total cost when keeping a high rate of accuracy. The cost of carrying out actions and getting observations is the dominated expense in online learning. Therefore, we design a cost-sensitive algorithm to learn action models under partial observability. It combines three techniques to lessen the total cost: constraints, filtering and active learning. These techniques are used in observation reduction in action model learning. First, the algorithm uses constraints to confine the observation space. Second, it removes unnecessary observations by belief state filtering. Third, it actively picks up observations based on the results of the previous two techniques. This paper also designs strategies to reduce the amount of plan steps used in the learning. We performed experiments on some benchmark domains. It shows two results. For one thing, the learning accuracy is high in most cases. For the other, the algorithm dramatically reduces the total cost according to the definition of cost in this paper. Therefore, it is significant for real-world learners, especially, when long plans are unavailable or observations are expensive.

ON DETERMINING SUITABLE SUBSETS OF DECISION RULES USING CHOQUET INTEGRAL

2008 ◽  
Vol 22 (02) ◽  
pp. 207-232 ◽  
Author(s):  
JAN RENDEK ◽  
LAURENT WENDLING
Keyword(s):  
Experimental Study ◽  
Decision Rule ◽  
Real World ◽  
Choquet Integral ◽  
Learning Algorithm ◽  
Decision Rules ◽  
Real World Data ◽  
Selection Scheme ◽  
World Data ◽  
Key Points

We present an approach to automatically extract a pertinent subset of soft output classifiers, and to aggregate them into a global decision rule using the Choquet integral. This approach relies on two key points. The first is a learning algorithm that uses a measure of the confusion between the categories to be recognized. The second is a selection scheme that discards weak or redundant decision rules, keeping only the most relevant subset. An experimental study, based on real world data, is then described. It analyzes the improvements achieved by these points first when used independently, then when combined together.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

scholarly journals Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1549
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder ◽  
Aletha B. Carson ◽  
Christian Junge ◽  
David E. Allen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
True Positive Rate ◽  
Training Dataset ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Activity Monitors

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

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