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AU2021104628A4 - A novel machine learning technique for classification using deviation parameters - Google Patents

A novel machine learning technique for classification using deviation parameters Download PDF

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Publication number
AU2021104628A4
AU2021104628A4 AU2021104628A AU2021104628A AU2021104628A4 AU 2021104628 A4 AU2021104628 A4 AU 2021104628A4 AU 2021104628 A AU2021104628 A AU 2021104628A AU 2021104628 A AU2021104628 A AU 2021104628A AU 2021104628 A4 AU2021104628 A4 AU 2021104628A4
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classification
machine learning
deviation
technique
deviation parameters
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AU2021104628A
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Raj Jaiswal
Mahesh Mahajan
Prasad Mahale
Makrand Mali
Nitin Patil
Shakil Pinjari
Sandip Sonawane
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
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  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

A NOVEL MACHINE LEARNING TECHNIQUE FOR CLASSIFICATION USING DEVIATION PARAMETERS The present invention relates to a novel machine learning technique for classification using deviation parameters. The proposed invention is useful for classification of numerical and categorical data.Machine learning is widely used in various domains in recent years. Machine learning algorithms are well investigated by researchers and devise various models. We propose a machine learning technique for classification based on deviation parameters. Proposed model is very effective in terms of classification. Performance of proposed deviation-based classification technique 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

A NOVEL MACHINE LEARNING TECHNIQUE FOR CLASSIFICATION USING DEVIATION PARAMETERS
Technical field of invention:
Present invention, in general, relates to the field of artificial intelligence and machine learning and more specifically to a novel machine learning technique for classification using deviation parameters which precisely use for classification purpose.
Background oftheinvention:
The background information herein below relates to the present disclosure but is not necessarily prior art.
The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?"
Modern 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 novel machine learning technique for classification using deviation parameters.
The present invention is useful for classification of numerical and categorical data.
It is therefore an object of the invention to helpclassification along with existing machine learning techniques.
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 novel machine learning technique for classification using deviation parameters. The object of the present invention is to provide classification of numerical and categorical data.The present invention 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.
Detailed description of the invention:
The present invention relates to a novel machine learning technique 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 xiI-xtI 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 (2)

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS
1. A novel machine learning technique for classification using deviation parameters wherein a deviation-based instance-based classifier used for classification purpose herein a data set D, Number of attributes as n, Total number of record N are used as an input.
2. The novel machine learning technique for classification using deviation parameters as claimed in claim 1 wherein the method consisting of following steps; a) Input dataset D. b) Separate the data based on class labels of the training samples. (Make a data partition for each class) c) For each data partition calculate the deviation parameter for each attribute d) Label a data tuple who is having majority for smaller values of average of deviation parameters
AU2021104628A 2021-07-27 2021-07-27 A novel machine learning technique for classification using deviation parameters Ceased AU2021104628A4 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2021104628A AU2021104628A4 (en) 2021-07-27 2021-07-27 A novel machine learning technique for classification using deviation parameters

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
AU2021104628A AU2021104628A4 (en) 2021-07-27 2021-07-27 A novel machine learning technique for classification using deviation parameters

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AU2021104628A4 true AU2021104628A4 (en) 2022-04-28

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