AU2021107101A4 - A machine learning based system for classification using deviation parameters - Google Patents
A machine learning based system for classification using deviation parameters Download PDFInfo
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- AU2021107101A4 AU2021107101A4 AU2021107101A AU2021107101A AU2021107101A4 AU 2021107101 A4 AU2021107101 A4 AU 2021107101A4 AU 2021107101 A AU2021107101 A AU 2021107101A AU 2021107101 A AU2021107101 A AU 2021107101A AU 2021107101 A4 AU2021107101 A4 AU 2021107101A4
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Abstract
A MACHINE LEARNING BASED SYSTEM FOR CLASSIFICATION USING
DEVIATION PARAMETERS
The present invention relates to a machine learning based system for classification using
deviation parameters. Proposed system is very effective in terms of classification. Performance
of proposed deviation-based classification system is better than some traditional machine
learning techniques like Decision tree, Random Forest etc. Deviation based learning is type of
instance-based learning, or lazy learning where average of deviation is calculated. The average
value of all classes for all respective attributes is calculated. Class prediction is done by majority
voting.
Description
Technical field of invention:
Present invention, in general, relates to the field of machine learning and more specifically to a novel a machine learning based system for classification using deviation parameters.
Background of the invention:
The background information herein below relates to the present disclosure but is not necessarily prior art.
Modem day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. Whereas, a machine learning algorithm for stock trading may inform the trader of future potential predictions.
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Although various attempts are made before, for providing various machine learning technique for classification using deviation parameters and few of them are such as US 20140343396A1 discloses use of machine learning for classification of magneto cardiograms, W02005002313A2 discloses machine learning for classification of magneto cardiograms
There exist many drawbacks in the existing unit or technology. Therefore, there is a need to introduce a novel machine learning technique for classification using deviation parameters. Hence the present invention develops a novel machine learning technique for classification.
Objective of the invention
An objective of the present invention is to attempt to overcome the problems of prior art and provide a machine learning based system for classification using deviation parameters
These and other objects and characteristics of the present invention will become apparent from the further disclosure to be made in the detailed description given below.
Summary of the invention:
Accordingly, the following invention provides a a machine learning based system for classification using deviation parameters In Present invention, a novel deviation-based instance-based classifier tool implemented for classification purpose. Performance of proposed system is better than some of the existing machine learning algorithms, so proposed lazy learner will be used with existing methods.
Detailed description of the invention:
Exemplary embodiments now will be described with reference to the accompanying drawings. The invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey its scope to those skilled in the art.
The present invention relates to a machine learning based system for classification using deviation parameters. Machine learning techniques are available for classification of numerical and categorical data, proposed method can be used for classification purpose with good performance. Deviation based classification can be used for classification purpose along with existing machine learning techniques. Accuracy of proposed classifier is tested on The Cleveland heart disease dataset at the University of California Irvine (UCI). UCI is repository for Machine Learning datasets. Proposed technique can be used for classification purpose.
In Present invention, a novel deviation-based instance-based classifier can be used for classification purpose. Performance of proposed technique is better than some of the existing machine learning algorithms, so proposed lazy learner will be used with existing methods. Thus proposed model will give good results as compare to some of existing methods.
A novel machine learning technique for classification using deviation parameters.
Input: Data set D, Number of attributes as n, Total number of record N.
Method: 1. Input dataset D. 2. Separate the data based on class labels of the training samples. (Make a data partition for each class) 3. For each data partition calculate the deviation parameter for each attribute zJXi-XtJ a =NN
4. Label a data tuple who is having majority for smaller values of average of deviation parameters
Output: Predicted class label for testing dataset
The many features and advantages of the invention are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention. Further, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
Claims (3)
1. A machine learning based system for classification using deviation parameters wherein a deviation based classification can be used for classification purpose along with existing machine learning configuration.
2. The machine learning based system for classification using deviation parameters as claimed in claim 1 wherein performance of proposed technique is better than all the existing machine learning algorithms.
3. The machine learning based system for classification using deviation parameters as claimed in claim 1 wherein the method comprises of following steps; • Input dataset D.
• Separate the data based on class labels of the training samples. (Make a data partition for each class) • For each data partition calculate the deviation parameter for each attribute
|Xi - Xt| N
• Label a data tuple who is having majority for smaller values of average of deviation parameters
• Predicted class label for testing dataset
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021107101A AU2021107101A4 (en) | 2021-08-25 | 2021-08-25 | A machine learning based system for classification using deviation parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021107101A AU2021107101A4 (en) | 2021-08-25 | 2021-08-25 | A machine learning based system for classification using deviation parameters |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021107101A4 true AU2021107101A4 (en) | 2021-12-02 |
Family
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021107101A Ceased AU2021107101A4 (en) | 2021-08-25 | 2021-08-25 | A machine learning based system for classification using deviation parameters |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021107101A4 (en) |
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2021
- 2021-08-25 AU AU2021107101A patent/AU2021107101A4/en not_active Ceased
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| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |