# Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Authors: Xiaoyu Yan; Shuo Ma
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.


---

## Body

## 1. Introduction

As an indispensable product for people, clothing has the combined attributes of physiological protection, mobility support, and self-expression [1]. As a result, the design of clothing is based on a combination of protection, fit, personal preference, and social aesthetics. With the rapid economic growth of recent years and the rapid development of clothing-related industries, the supply of clothing products has been greatly enriched [2]. People's demands for clothing have long gone beyond physiological and safety needs to focus on social needs, personality preferences, and self-realisation. The choice of clothing, in terms of style, stylistic details, and size, has gradually changed from a simple, comfortable, mass-market design to a personalised design that is based on fit, self-fashioning, and self-fulfillment [3]. With the widespread popularity of e-commerce, consumers' demand for personalised products has become increasingly strong [4]. In this context, traditional garment customisation models are no longer able to meet the needs of consumers due to their long production cycles and monotonous styles [5]. As a result, there is a need to provide a personalised apparel design model to achieve zero distance interaction with the customer, thus reducing production costs for the company and increasing customer satisfaction. The personalisation process must be supported by detailed theoretical methods and technical guidance [6]. The modern model of garment design consists mainly of style design, structure design, and process design. The main task of style design is to draw up renderings and style drawings, the main task of structural design is to development patterns, and the main task of process design is to produce process guidance documents [7, 8]. As shown in Figure 1, style design, structure design, and process design are interlinked and they can form the main part of modern garment design. The needs of consumers in the market have changed with the rise in material and cultural standards of living, from the traditional economical to the enjoyable. As a result, traditional mass production methods are no longer able to meet consumers' needs for fashion and individuality [9]. Today, in the pursuit of individuality and fashion, people have new requirements for bespoke garment designs that inspire confidence and satisfaction. Consumers want to participate in the customisation process, expressing their needs through interactive customisation and being able to customise the style of their clothing according to their own wishes [10]. In this way, consumers are better able to personalise their own designs independently and thus create personalised garments for themselves that match their own aesthetics, which has been the most popular industry chain strategy in recent years.Figure 1
Relationship among style design, structure design, and process design.China is currently the world's largest producer, consumer, and exporter of textiles and clothing. As a result, the garment industry plays a vital role in China's national economy [11]. The realisation of intelligent manufacturing in apparel enterprises is of great significance to the upgrading of China's overall manufacturing level. Also, the realisation of intelligent garment design is an important part of the implementation of intelligent manufacturing in apparel enterprises [12]. However, the garment industry is typically a low-tech and labour-intensive industry. With the rapid development of the economy, the consumption habits of customers are constantly changing and the demand for personalisation is increasing [13]. In this context, garment companies need to constantly improve the efficiency of their product development in order to cope with the fierce competition in the market [14, 15]. Over the past hundred years, the production model of apparel companies has evolved from artisanal production to mass production and then to mass customisation in order to keep pace with market changes and customer needs. Mass customisation requires garment manufacturers to produce customised garment products quickly and in large quantities to suit different customer sizes and individual requirements [16]. However, the individual needs of customers increase the difficulty of developing apparel products and prolong the product development cycle. The fast-changing market requires apparel companies to react quickly and shorten the product development cycle as much as possible [17]. In other words, there is always a serious conflict between the individual needs of customers and the demands of the market on product development cycles. In the future, the market will change even more dramatically and consumers' personalisation needs will tend to increase [3]. The question of how to improve the efficiency of apparel product development and shorten the product development cycle while satisfying consumer needs has been a difficult one for apparel companies. There are currently two ways to solve this problem. Firstly, more apparel product developers could be hired to work on the design and production of garments [18]. In addition, the current design methods and processes could be revamped. Hiring more experienced apparel product developers is currently the main solution to this problem, but this approach would significantly increase the cost of product development and would not be conducive to a competitive position in the marketplace [19]. It is clear that optimising and innovating design methods and processes is an effective way of overcoming these challenges in product development for apparel companies.Clothing is an essential part of the culture of human society and an important expression of human spiritual civilisation [20]. Although the domestic garment industry has been developing for 30 to 40 years, it still seems to be a relatively traditional industry. The traditional domestic garment industry is labour-intensive and has always suffered from low production levels, serious homogenisation of products, and low brand awareness. In recent years, with the emergence and rapid development of big data [21], cloud computing [22], intelligent manufacturing [23], and e-commerce [24], it has become possible to shift from high-end tailoring to large-scale personalised tailoring. This possibility will accelerate the transformation of the garment industry towards the Internet. This breakthrough in the traditional thinking of the manufacturing industry has led to a shift from a closed manufacturing system to an open, direct-to-consumer production model, allowing consumers to participate directly in the design and production of garments. This type of consumer participation in personalisation has led to the development of a more dynamic apparel industry and an optimised industrial structure. Large-scale personalised tailoring is set to become an important trend in the future development of clothing [25]. The traditional garment design process is shown in Figure 2. This process can be generally divided into three parts: design object determination, garment concept design, and garment production. The first step is to analyse the customer's needs and to define the design plan and concept. While collecting the source material around the design concept and plan, the customer's body size information is collected. A series of variations on the source material is then drawn up, based on the designer's inspiration and experience, to produce a graphic style drawing, which includes the basic elements of the design. Furthermore, the shape of the silhouette, structure, and details of the garment are constructed by dividing, combining, building up, and arranging the three elements. The garment structure is then transformed from a flat to a three-dimensional form by using flat or three-dimensional cutting methods. The three-dimensional shape is then evaluated and fed back into the design for correction and adjustment. Finally, the output is used to assess the feasibility of the finished garment and to adjust the garment structure.Figure 2
Traditional garment design process.The apparel product development department is one of the core departments of an apparel company [26]. The quality of an apparel product depends to a large extent on the skill level of the product developer. Traditionally, garment design and pattern development is a highly empirical task. After all, a junior apparel product developer needs several years of practical experience to become proficient in apparel design or pattern development. Experienced designers and pattern makers are therefore a scarce resource for an apparel company, and their departure can have a serious impact on the company [27]. During the product development phase, designers, pattern makers, and craftsmen need to communicate and collaborate with each other repeatedly in order to develop a satisfactory garment. This process is tedious and time-consuming and requires the product developer to have a wealth of experience and knowledge. Therefore, there is a pressing need for apparel companies to move away from the traditional overreliance on experienced designers and pattern makers and to be able to develop satisfactory garment products quickly. This will reduce the reliance on designers and pattern makers and increase the efficiency of product development and reduce development costs. The fit of garments is always one of the main concerns of apparel developers and consumers [28]. Fit assessment is a constant part of the apparel product development process, with designers repeatedly trying on samples to check whether a garment is feasible and whether it fits properly. Currently, the fit of a garment can only be analysed by trying on a real garment in person or on a human platform. This process is cumbersome and can significantly increase the cost of developing a garment. While the advent of virtual fitting technology can assist apparel developers in checking the viability of a garment, it cannot effectively determine the fit of a garment. As a result, it is quite important for product development to be able to assess the fit of garments without the need for a real fitting.With the rapid development of computer information technology in recent years, artificial intelligence [29], machine learning [30], expert systems [31], and system dynamics [32] have been widely used in various industries. However, in the apparel industry, particularly in the area of apparel design and fit assessment, the use of computers is not yet sufficiently advanced. The main reason for this is the uncertainty and inaccuracy of knowledge about garment design and fit assessment. After all, this type of knowledge is more difficult to extract and represent by computer. Recent research has shown that the tacit knowledge involved in apparel design can also be extracted, expressed, and applied through mathematical modelling and computer simulation. It is in this context that this study proposes a model for apparel design based on the Bayesian classifier as well as decision tree algorithm. The model allows for the mathematical modelling of the expert knowledge required for garment design, thus enabling less experienced designers to develop garment products and assess their fit quickly and efficiently.


## 2. Relevant Algorithm

Mathematical modelling of garment design is the basis for the automation and intelligence of apparel design. Currently, linear regression models are widely used for knowledge modelling in the field of garment design and pattern development, mainly because they are simple to understand and practical. However, there are many aspects of garment design that do not lend themselves to the use of linear regression for knowledge modelling. As a result, this section mainly introduces Bayesian classifiers, decision trees, and neural network algorithms to provide the necessary theoretical support for the construction of garment design models.


### 2.1. Feature Selection

The main function of feature selection is to eliminate the features with interference factors and find the information feature subset with the least dimension but stronger discrimination ability. With the rapid growth of data scale, the sample size increases and the dimension of feature becomes higher and higher. Furthermore, with the rapid growth of irrelevant features or noise data, the effectiveness of the algorithm gradually decreases. As a result, feature selection is quite necessary, which can select the most effective feature from the original data, so as to reduce the dimension of dataset and improve the performance of learning algorithm. Figure3 shows the basic framework for feature selection.Figure 3
Basic framework for feature selection.


### 2.2. Bayesian Classifier

Most datasets contain a large number of features, and when training a dataset for classification, it can be seen that the degree to which the features contained in the dataset affect the classification results varies. Generally, the more features there are, the better the classification results are. However, much of the data in the database are unprocessed and contain too many confounding factors that can affect the classification results to some extent, and feature selection can solve this problem. As a result, before using the Bayesian classifier algorithm, feature selection is required in order to obtain a better performance of the trained classifier.Bayesian classifiers are machine learning algorithms built on the basis of Bayes' theorem. As an early type of Bayesian classifier, the plain Bayesian classifier has high classification or prediction efficiency and good adaptation to new samples. The plain Bayesian classifier treats the properties of classes as isolated and independent of each other, i.e., the class conditions are independent of each other. This hypothesis allows for the handling of otherwise complex class-attribute relationships, and although some data information is lost, the amount of arithmetic involved in the classification process is significantly reduced.LetS=s1,s2,s3,…,sn denote the training dataset, which contains n samples. Also, let si=di1,di2,di3,…,dim,ki represent the non-category and category attribute values of the ith sample in the training dataset. Therefore, the detailed implementation process can be seen as follows:(1)
Construct the structure of Bayesian classifier.According to the different values of the class attributes of the training samples, the root node of the non-class attributes is used to construct the structure of the Bayesian model.(2)
Construct parameter table.According to the training datasetS=s1,s2,s3,…,sn and the structure of Bayesian classifier, the relevant parameters can be learned and the parameter table can be constructed.(3)
Calculate posteriori probability.For a sampled1,d2,…,dm, the calculation process of its posteriori probability is shown as follows:(1)Pki|d=Pki|d×PkiPd=θ×Pki×∏j=1mPd1|ki,whereθ refers to the regular factor of one constant and ki refers to the value of the category variable.(4)
Distribution of samples.After obtaining the posteriori probability, the samples can be distributed to the specified attribute.To sum up, the detailed implementation process of applying the Bayesian classifier can be seen in Figure4.Figure 4
Implementation process of applying Bayesian classifier.


### 2.3. Decision Tree

As shown in Figure5, decision tree is a knowledge representation method similar to tree organization. All non-leaf nodes of a decision tree represent the non-category attributes of sample data, while all leaf nodes represent the category attributes of sample data. Any unbranched branch in a decision tree, from root to branch to leaf, represents a knowledge rule. The method of knowledge expression and application of decision tree is to make recursive judgment from the root node according to each attribute value of the sample to be evaluated until the leaf node is given to belong to a certain category.Figure 5
Example of decision tree.Decision tree algorithm uses information entropy and gain degree in information theory as classification basis to study the classification of things or events. The adequacy of information transfer depends on the degree of uncertainty of the system. The smaller the uncertainty of the system is, the more sufficient the information transfer will be, and vice versa. Therefore, in order to transmit information adequately, system uncertainty must be ensured as little as possible. The decision tree algorithm applies the information gain value as the criterion to judge the uncertainty of the system, selects the maximum attribute of the information gain value as the test attribute, and divides the training sample set according to the different values of the test attribute. The calculation method of information gain value is given below.LetD represent the set of d training samples, and let a denote the number of attribute values. Then, calculation of information entropy is shown below:(2)Id1,d2,…,da=−∑i=1aPi×lnPi,where Pi refers to the probability of the ith sample.Next, the calculation of information entropy generated by dividing samples by attributeX is shown below:(3)YX=∑i=1md1i+d2i+…+dmiD×Id1,d2,…,da.Finally, the calculation of the information gain value generated by dividing the sample sets by attribute X is shown below:(4)GX=Id1,d2,…,da−YX.When machine learning algorithm is used to construct classification model, it is necessary to reserve some samples in the total sample set for testing and verifying the prediction accuracy of the built model.


### 2.4. Neural Network

The human brain is an advanced intelligent signal processing system, which consists of a large number of interconnected neurons, each of which has a series of processing and transmission of electrochemical signals it receives. The electrochemical signal transmission process is as follows. First, the synapse receives a large number of electrochemical signals, which are transmitted through the dendrites to the cell body. The inhibitory and stimulating parts of the electrochemical signal are then superimposed on each other. When the cumulative effect reaches and exceeds a certain threshold, the cell body is excited and outputs an electrochemical signal. Finally, the electrochemical signal is transmitted from the axon to the adjacent neurons, and so on. The neural network structure of the human brain is made up of two parts: nature and learning. In the continuous learning process of external things, some links between neuron cells will slowly disappear, and when these existing links disappear, a large number of new links will be generated. The neural network of the brain continuously learns new knowledge from real things, gradually changes the structure of the network, and can produce new cognition of things.Artificial neural network is an intelligent information processing system based on the working principle of brain neural network. In the construction process of artificial neural network, it is necessary to simulate the main functions of biological neuron cells first and construct the human neuron model with similar functions. As shown in Figure6, the output function of the artificial neuron model has various forms.Figure 6
Output function of the artificial neuron model.
(a)(b)(c)


## 2.1. Feature Selection

The main function of feature selection is to eliminate the features with interference factors and find the information feature subset with the least dimension but stronger discrimination ability. With the rapid growth of data scale, the sample size increases and the dimension of feature becomes higher and higher. Furthermore, with the rapid growth of irrelevant features or noise data, the effectiveness of the algorithm gradually decreases. As a result, feature selection is quite necessary, which can select the most effective feature from the original data, so as to reduce the dimension of dataset and improve the performance of learning algorithm. Figure3 shows the basic framework for feature selection.Figure 3
Basic framework for feature selection.


## 2.2. Bayesian Classifier

Most datasets contain a large number of features, and when training a dataset for classification, it can be seen that the degree to which the features contained in the dataset affect the classification results varies. Generally, the more features there are, the better the classification results are. However, much of the data in the database are unprocessed and contain too many confounding factors that can affect the classification results to some extent, and feature selection can solve this problem. As a result, before using the Bayesian classifier algorithm, feature selection is required in order to obtain a better performance of the trained classifier.Bayesian classifiers are machine learning algorithms built on the basis of Bayes' theorem. As an early type of Bayesian classifier, the plain Bayesian classifier has high classification or prediction efficiency and good adaptation to new samples. The plain Bayesian classifier treats the properties of classes as isolated and independent of each other, i.e., the class conditions are independent of each other. This hypothesis allows for the handling of otherwise complex class-attribute relationships, and although some data information is lost, the amount of arithmetic involved in the classification process is significantly reduced.LetS=s1,s2,s3,…,sn denote the training dataset, which contains n samples. Also, let si=di1,di2,di3,…,dim,ki represent the non-category and category attribute values of the ith sample in the training dataset. Therefore, the detailed implementation process can be seen as follows:(1)
Construct the structure of Bayesian classifier.According to the different values of the class attributes of the training samples, the root node of the non-class attributes is used to construct the structure of the Bayesian model.(2)
Construct parameter table.According to the training datasetS=s1,s2,s3,…,sn and the structure of Bayesian classifier, the relevant parameters can be learned and the parameter table can be constructed.(3)
Calculate posteriori probability.For a sampled1,d2,…,dm, the calculation process of its posteriori probability is shown as follows:(1)Pki|d=Pki|d×PkiPd=θ×Pki×∏j=1mPd1|ki,whereθ refers to the regular factor of one constant and ki refers to the value of the category variable.(4)
Distribution of samples.After obtaining the posteriori probability, the samples can be distributed to the specified attribute.To sum up, the detailed implementation process of applying the Bayesian classifier can be seen in Figure4.Figure 4
Implementation process of applying Bayesian classifier.


## 2.3. Decision Tree

As shown in Figure5, decision tree is a knowledge representation method similar to tree organization. All non-leaf nodes of a decision tree represent the non-category attributes of sample data, while all leaf nodes represent the category attributes of sample data. Any unbranched branch in a decision tree, from root to branch to leaf, represents a knowledge rule. The method of knowledge expression and application of decision tree is to make recursive judgment from the root node according to each attribute value of the sample to be evaluated until the leaf node is given to belong to a certain category.Figure 5
Example of decision tree.Decision tree algorithm uses information entropy and gain degree in information theory as classification basis to study the classification of things or events. The adequacy of information transfer depends on the degree of uncertainty of the system. The smaller the uncertainty of the system is, the more sufficient the information transfer will be, and vice versa. Therefore, in order to transmit information adequately, system uncertainty must be ensured as little as possible. The decision tree algorithm applies the information gain value as the criterion to judge the uncertainty of the system, selects the maximum attribute of the information gain value as the test attribute, and divides the training sample set according to the different values of the test attribute. The calculation method of information gain value is given below.LetD represent the set of d training samples, and let a denote the number of attribute values. Then, calculation of information entropy is shown below:(2)Id1,d2,…,da=−∑i=1aPi×lnPi,where Pi refers to the probability of the ith sample.Next, the calculation of information entropy generated by dividing samples by attributeX is shown below:(3)YX=∑i=1md1i+d2i+…+dmiD×Id1,d2,…,da.Finally, the calculation of the information gain value generated by dividing the sample sets by attribute X is shown below:(4)GX=Id1,d2,…,da−YX.When machine learning algorithm is used to construct classification model, it is necessary to reserve some samples in the total sample set for testing and verifying the prediction accuracy of the built model.


## 2.4. Neural Network

The human brain is an advanced intelligent signal processing system, which consists of a large number of interconnected neurons, each of which has a series of processing and transmission of electrochemical signals it receives. The electrochemical signal transmission process is as follows. First, the synapse receives a large number of electrochemical signals, which are transmitted through the dendrites to the cell body. The inhibitory and stimulating parts of the electrochemical signal are then superimposed on each other. When the cumulative effect reaches and exceeds a certain threshold, the cell body is excited and outputs an electrochemical signal. Finally, the electrochemical signal is transmitted from the axon to the adjacent neurons, and so on. The neural network structure of the human brain is made up of two parts: nature and learning. In the continuous learning process of external things, some links between neuron cells will slowly disappear, and when these existing links disappear, a large number of new links will be generated. The neural network of the brain continuously learns new knowledge from real things, gradually changes the structure of the network, and can produce new cognition of things.Artificial neural network is an intelligent information processing system based on the working principle of brain neural network. In the construction process of artificial neural network, it is necessary to simulate the main functions of biological neuron cells first and construct the human neuron model with similar functions. As shown in Figure6, the output function of the artificial neuron model has various forms.Figure 6
Output function of the artificial neuron model.
(a)(b)(c)


## 3. Garment Design Model

### 3.1. Style and Structure Correlation Design

The style diagram is used to express the designer's design concept in concrete terms. The structure of a garment is a flat pattern presentation of the garment pieces based on the style diagram. There is a close relationship between the design of the garment style and the construction of the garment, but currently in the garment industry, the two are separate departments, with the designer designing the style and the pattern maker designing the construction. In the product development phase, designers need to communicate with pattern makers in order to avoid poor understanding of styles. The separation of the design and structure of the garment is one of the main reasons for the long product development cycle. If the design and structure of the garment were integrated, the development cycle would be much shorter. To be specific, the stye and structure correlation design can be seen in Figure7.Figure 7
Stye and structure correlation design.


### 3.2. Construction of Mathematical Model

How to obtain satisfactory clothing style is also a problem worth considering. However, due to the complexity of clothing style, the coding scheme based on spline key point parameters has great instability. Therefore, the model can not only generate new styles but also get user satisfaction.Body size is the basis of clothing structure design, and clothing style design is based on the comparison of clothing and human body, without measuring the real body size. If the body size is also given to the pattern drawing, then there is a link between the design and structural design of clothing. The basis of the association design technology between garment style and garment structure proposed in this chapter is to assign human body size to the pattern drawing, so that the garment style design and structural design are integrated based on the common human body size. Compared with the traditional fashion design method, this method is a novel design method. Therefore, the design idea of multi-factor combination-driven is based on the analysis of the essence of things to the characteristics and forms of elements, and its methods and related technologies have a wide range of applications. However, in the aspect of clothing design, especially those requiring individuation, most current studies regard the correlation between elements as the influence of one or several parameters and determine these parameters through experience or data fitting. This single reasoning method is difficult to reveal the interaction of different elements and has great limitations.According to the function and use of clothing, people's perception of clothing can be divided into functional image and emotional image. Functional image mainly expresses the perception of specific functional strength of clothing. Emotional image can meet the needs of functional image on the premise of a certain clothing design, if only the modelling is consistent with the user’s personal preferences. Compared with other elements of clothing, specific elements and styles have a greater impact on specific user image and present a stable state of psychological association. Therefore, the research on users' clothing image preference can fully explore the clothing elements and the rules of modelling construction and design the clothing to meet the personalised expectation.


## 3.1. Style and Structure Correlation Design

The style diagram is used to express the designer's design concept in concrete terms. The structure of a garment is a flat pattern presentation of the garment pieces based on the style diagram. There is a close relationship between the design of the garment style and the construction of the garment, but currently in the garment industry, the two are separate departments, with the designer designing the style and the pattern maker designing the construction. In the product development phase, designers need to communicate with pattern makers in order to avoid poor understanding of styles. The separation of the design and structure of the garment is one of the main reasons for the long product development cycle. If the design and structure of the garment were integrated, the development cycle would be much shorter. To be specific, the stye and structure correlation design can be seen in Figure7.Figure 7
Stye and structure correlation design.


## 3.2. Construction of Mathematical Model

How to obtain satisfactory clothing style is also a problem worth considering. However, due to the complexity of clothing style, the coding scheme based on spline key point parameters has great instability. Therefore, the model can not only generate new styles but also get user satisfaction.Body size is the basis of clothing structure design, and clothing style design is based on the comparison of clothing and human body, without measuring the real body size. If the body size is also given to the pattern drawing, then there is a link between the design and structural design of clothing. The basis of the association design technology between garment style and garment structure proposed in this chapter is to assign human body size to the pattern drawing, so that the garment style design and structural design are integrated based on the common human body size. Compared with the traditional fashion design method, this method is a novel design method. Therefore, the design idea of multi-factor combination-driven is based on the analysis of the essence of things to the characteristics and forms of elements, and its methods and related technologies have a wide range of applications. However, in the aspect of clothing design, especially those requiring individuation, most current studies regard the correlation between elements as the influence of one or several parameters and determine these parameters through experience or data fitting. This single reasoning method is difficult to reveal the interaction of different elements and has great limitations.According to the function and use of clothing, people's perception of clothing can be divided into functional image and emotional image. Functional image mainly expresses the perception of specific functional strength of clothing. Emotional image can meet the needs of functional image on the premise of a certain clothing design, if only the modelling is consistent with the user’s personal preferences. Compared with other elements of clothing, specific elements and styles have a greater impact on specific user image and present a stable state of psychological association. Therefore, the research on users' clothing image preference can fully explore the clothing elements and the rules of modelling construction and design the clothing to meet the personalised expectation.


## 4. Conclusion

This study states the intelligent clothing design system, starting from the research point of clothing style, and human-machine interaction as a main way of clothing innovation design. According to the analysis of users' needs, the style is described, and the style of clothing is studied and analysed to achieve the expression of users' needs. Combined with the characteristics of men's suit design based on biological genes, Bayesian classifier and decision tree algorithm are used to design the suit. Neural network technology was used to extract and express the gene of clothing style samples to obtain the main component gene characteristics of clothing style. Then, this research analyses its characteristics and raises up a kind of new gene definition, sorts out the influence clothing design element, as well as carries on the parametric design.The research in this paper provides a certain theoretical basis for clothing design and fit evaluation. However, in view of the limitations of knowledge level and research conditions, the following aspects should be explored in the later stage to further improve the research content. First of all, subsequent studies can collect different body size data for different customer groups. In addition, the data are used to construct the correlation design model of clothing style and clothing structure, so that the final design of clothing products will be more targeted. Furthermore, a series of dynamic clothing pressures can be measured, and the dynamic clothing pressures with time series characteristics can be taken as the input of the model. In this case, the prediction accuracy and reliability of garment fit evaluation model based on machine learning are expected to be further improved.



---

*Source: 1904158-2022-05-27.xml*